NRC Systems for Low Resource German-Upper Sorbian Machine Translation 2020: Transfer Learning with Lexical Modifications
Rebecca Knowles, Samuel Larkin, Darlene Stewart, Patrick Littell
Abstract
We describe the National Research Council of Canada (NRC) neural machine translation systems for the German-Upper Sorbian supervised track of the 2020 shared task on Unsupervised MT and Very Low Resource Supervised MT. Our models are ensembles of Transformer models, built using combinations of BPE-dropout, lexical modifications, and backtranslation.- Anthology ID:
- 2020.wmt-1.132
- Volume:
- Proceedings of the Fifth Conference on Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1112–1122
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.132
- DOI:
- Cite (ACL):
- Rebecca Knowles, Samuel Larkin, Darlene Stewart, and Patrick Littell. 2020. NRC Systems for Low Resource German-Upper Sorbian Machine Translation 2020: Transfer Learning with Lexical Modifications. In Proceedings of the Fifth Conference on Machine Translation, pages 1112–1122, Online. Association for Computational Linguistics.
- Cite (Informal):
- NRC Systems for Low Resource German-Upper Sorbian Machine Translation 2020: Transfer Learning with Lexical Modifications (Knowles et al., WMT 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.wmt-1.132.pdf