Abstract
In this paper, we describe the system used for our first participation at the CoNLL 2018 shared task. The submitted system largely reused the state of the art parser from CoNLL 2017 (https://github.com/tdozat/Parser-v2). We enhanced this system for morphological features predictions, and we used all available resources to provide accurate models for low-resource languages. We ranked 5th of 27 participants in MLAS for building morphology aware dependency trees, 2nd for morphological features only, and 3rd for tagging (UPOS) and parsing (LAS) low-resource languages.- Anthology ID:
- K18-2003
- 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:
- 34–44
- Language:
- URL:
- https://aclanthology.org/K18-2003
- DOI:
- 10.18653/v1/K18-2003
- Cite (ACL):
- Elie Duthoo and Olivier Mesnard. 2018. CEA LIST: Processing Low-Resource Languages for CoNLL 2018. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 34–44, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- CEA LIST: Processing Low-Resource Languages for CoNLL 2018 (Duthoo & Mesnard, CoNLL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/K18-2003.pdf
- Code
- tdozat/Parser-v2