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
- Editors:
- Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Yvette Graham, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri
- 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/emnlp-22-attachments/2020.wmt-1.132.pdf