Abstract
This paper describes the Tencent AI Lab submission of the WMT2020 shared task on biomedical translation in four language directions: German<->English, English<->German, Chinese<->English and English<->Chinese. We implement our system with model ensemble technique on different transformer architectures (Deep, Hybrid, Big, Large Transformers). To enlarge the in-domain bilingual corpus, we use back-translation of monolingual in-domain data in the target language as additional in-domain training data. Our systems in German->English and English->German are ranked 1st and 3rd respectively according to the official evaluation results in terms of BLEU scores.- Anthology ID:
- 2020.wmt-1.97
- 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:
- 881–886
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.97
- DOI:
- Cite (ACL):
- Xing Wang, Zhaopeng Tu, Longyue Wang, and Shuming Shi. 2020. Tencent AI Lab Machine Translation Systems for the WMT20 Biomedical Translation Task. In Proceedings of the Fifth Conference on Machine Translation, pages 881–886, Online. Association for Computational Linguistics.
- Cite (Informal):
- Tencent AI Lab Machine Translation Systems for the WMT20 Biomedical Translation Task (Wang et al., WMT 2020)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.wmt-1.97.pdf
- Code
- hsing-wang/wmt2020_biomedical