Abstract
This paper describes the Tencent AI Lab submission of the WMT2021 shared task on biomedical translation in eight language directions: English-German, English-French, English-Spanish and English-Russian. We utilized different Transformer architectures, pretraining and back-translation strategies to improve translation quality. Concretely, we explore mBART (Liu et al., 2020) to demonstrate the effectiveness of the pretraining strategy. Our submissions (Tencent AI Lab Machine Translation, TMT) in German/French/Spanish⇒English are ranked 1st respectively according to the official evaluation results in terms of BLEU scores.- Anthology ID:
- 2021.wmt-1.89
- 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:
- 874–878
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.89
- DOI:
- Cite (ACL):
- Xing Wang, Zhaopeng Tu, and Shuming Shi. 2021. Tencent AI Lab Machine Translation Systems for the WMT21 Biomedical Translation Task. In Proceedings of the Sixth Conference on Machine Translation, pages 874–878, Online. Association for Computational Linguistics.
- Cite (Informal):
- Tencent AI Lab Machine Translation Systems for the WMT21 Biomedical Translation Task (Wang et al., WMT 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.wmt-1.89.pdf