A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages

Clara Vania, Yova Kementchedjhieva, Anders Søgaard, Adam Lopez

Abstract
Parsers are available for only a handful of the world’s languages, since they require lots of training data. How far can we get with just a small amount of training data? We systematically compare a set of simple strategies for improving low-resource parsers: data augmentation, which has not been tested before; cross-lingual training; and transliteration. Experimenting on three typologically diverse low-resource languages—North Sámi, Galician, and Kazah—We find that (1) when only the low-resource treebank is available, data augmentation is very helpful; (2) when a related high-resource treebank is available, cross-lingual training is helpful and complements data augmentation; and (3) when the high-resource treebank uses a different writing system, transliteration into a shared orthographic spaces is also very helpful.
Anthology ID:
D19-1102
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1105–1116
Language:
URL:
https://aclanthology.org/D19-1102
DOI:
10.18653/v1/D19-1102
Bibkey:
Cite (ACL):
Clara Vania, Yova Kementchedjhieva, Anders Søgaard, and Adam Lopez. 2019. A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1105–1116, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages (Vania et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp-22-attachments/D19-1102.pdf
Attachment:
 D19-1102.Attachment.pdf
Data
Universal Dependencies
PDF Search Attachment