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
- 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)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/D19-1102.pdf
- Data
- Universal Dependencies