Generalized Data Augmentation for Low-Resource Translation
Mengzhou Xia, Xiang Kong, Antonios Anastasopoulos, Graham Neubig
Abstract
Low-resource language pairs with a paucity of parallel data pose challenges for machine translation in terms of both adequacy and fluency. Data augmentation utilizing a large amount of monolingual data is regarded as an effective way to alleviate the problem. In this paper, we propose a general framework of data augmentation for low-resource machine translation not only using target-side monolingual data, but also by pivoting through a related high-resource language. Specifically, we experiment with a two-step pivoting method to convert high-resource data to the low-resource language, making best use of available resources to better approximate the true distribution of the low-resource language. First, we inject low-resource words into high-resource sentences through an induced bilingual dictionary. Second, we further edit the high-resource data injected with low-resource words using a modified unsupervised machine translation framework. Extensive experiments on four low-resource datasets show that under extreme low-resource settings, our data augmentation techniques improve translation quality by up to 1.5 to 8 BLEU points compared to supervised back-translation baselines.- Anthology ID:
- P19-1579
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5786–5796
- Language:
- URL:
- https://aclanthology.org/P19-1579
- DOI:
- 10.18653/v1/P19-1579
- Cite (ACL):
- Mengzhou Xia, Xiang Kong, Antonios Anastasopoulos, and Graham Neubig. 2019. Generalized Data Augmentation for Low-Resource Translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5786–5796, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Generalized Data Augmentation for Low-Resource Translation (Xia et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P19-1579.pdf