CUNI systems for WMT21: Multilingual Low-Resource Translation for Indo-European Languages Shared Task
Josef Jon, Michal Novák, João Paulo Aires, Dusan Varis, Ondřej Bojar
Abstract
This paper describes Charles University sub-mission for Terminology translation shared task at WMT21. The objective of this task is to design a system which translates certain terms based on a provided terminology database, while preserving high overall translation quality. We competed in English-French language pair. Our approach is based on providing the desired translations alongside the input sentence and training the model to use these provided terms. We lemmatize the terms both during the training and inference, to allow the model to learn how to produce correct surface forms of the words, when they differ from the forms provided in the terminology database.- Anthology ID:
- 2021.wmt-1.42
- Volume:
- Proceedings of the Sixth Conference on Machine Translation
- Month:
- November
- Year:
- 2021
- Address:
- Online
- Editors:
- Loic Barrault, Ondrej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussa, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Tom Kocmi, Andre Martins, Makoto Morishita, Christof Monz
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 354–361
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.42
- DOI:
- Cite (ACL):
- Josef Jon, Michal Novák, João Paulo Aires, Dusan Varis, and Ondřej Bojar. 2021. CUNI systems for WMT21: Multilingual Low-Resource Translation for Indo-European Languages Shared Task. In Proceedings of the Sixth Conference on Machine Translation, pages 354–361, Online. Association for Computational Linguistics.
- Cite (Informal):
- CUNI systems for WMT21: Multilingual Low-Resource Translation for Indo-European Languages Shared Task (Jon et al., WMT 2021)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2021.wmt-1.42.pdf
- Data
- OPUS-MT