Abstract
This paper describes the University of Maryland’s submission to the WMT 2019 Kazakh-English news translation task. We study the impact of transfer learning from another low-resource but related language. We experiment with different ways of encoding lexical units to maximize lexical overlap between the two language pairs, as well as back-translation and ensembling. The submitted system improves over a Kazakh-only baseline by +5.45 BLEU on newstest2019.- Anthology ID:
- W19-5308
- Volume:
- Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 134–140
- Language:
- URL:
- https://aclanthology.org/W19-5308
- DOI:
- 10.18653/v1/W19-5308
- Cite (ACL):
- Eleftheria Briakou and Marine Carpuat. 2019. The University of Maryland’s Kazakh-English Neural Machine Translation System at WMT19. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 134–140, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- The University of Maryland’s Kazakh-English Neural Machine Translation System at WMT19 (Briakou & Carpuat, WMT 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W19-5308.pdf