Abstract
This paper describes our dependency parsing system in CoNLL-2017 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. We primarily focus on the low-resource languages (surprise languages). We have developed a framework to combine multiple treebanks to train parsers for low resource languages by delexicalization method. We have applied transformation on source language treebanks based on syntactic features of the low-resource language to improve performance of the parser. In the official evaluation, our system achieves an macro-averaged LAS score of 67.61 and 37.16 on the entire blind test data and the surprise language test data respectively.- Anthology ID:
- K17-3019
- Volume:
- Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Jan Hajič, Dan Zeman
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 182–190
- Language:
- URL:
- https://aclanthology.org/K17-3019
- DOI:
- 10.18653/v1/K17-3019
- Cite (ACL):
- Ayan Das, Affan Zaffar, and Sudeshna Sarkar. 2017. Delexicalized transfer parsing for low-resource languages using transformed and combined treebanks. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 182–190, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Delexicalized transfer parsing for low-resource languages using transformed and combined treebanks (Das et al., CoNLL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/K17-3019.pdf
- Data
- Universal Dependencies