@inproceedings{wu-etal-2023-path,
title = "The Path to Continuous Domain Adaptation Improvements by {HW}-{TSC} for the {WMT}23 Biomedical Translation Shared Task",
author = "Wu, Zhanglin and
Wei, Daimeng and
Li, Zongyao and
Yu, Zhengzhe and
Li, Shaojun and
Chen, Xiaoyu and
Shang, Hengchao and
Guo, Jiaxin and
Xie, Yuhao and
Lei, Lizhi and
Yang, Hao and
Jiang, Yanfei",
editor = "Koehn, Philipp and
Haddow, Barry and
Kocmi, Tom and
Monz, Christof",
booktitle = "Proceedings of the Eighth Conference on Machine Translation",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wmt-1.27",
doi = "10.18653/v1/2023.wmt-1.27",
pages = "271--274",
abstract = "This paper presents the domain adaptation methods adopted by Huawei Translation Service Center (HW-TSC) to train the neural machine translation (NMT) system on the English↔German (en↔de) language pair of the WMT23 biomedical translation task. Our NMT system is built on deep Transformer with larger parameter sizes. Based on the biomedical NMT system trained last year, we leverage Curriculum Learning, Data Diversification, Forward translation, Back translation, and Transductive Ensemble Learning to further improve system performance. Overall, we believe our submission can achieve highly competitive result in the official final evaluation.",
}
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%0 Conference Proceedings
%T The Path to Continuous Domain Adaptation Improvements by HW-TSC for the WMT23 Biomedical Translation Shared Task
%A Wu, Zhanglin
%A Wei, Daimeng
%A Li, Zongyao
%A Yu, Zhengzhe
%A Li, Shaojun
%A Chen, Xiaoyu
%A Shang, Hengchao
%A Guo, Jiaxin
%A Xie, Yuhao
%A Lei, Lizhi
%A Yang, Hao
%A Jiang, Yanfei
%Y Koehn, Philipp
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Monz, Christof
%S Proceedings of the Eighth Conference on Machine Translation
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wu-etal-2023-path
%X This paper presents the domain adaptation methods adopted by Huawei Translation Service Center (HW-TSC) to train the neural machine translation (NMT) system on the English↔German (en↔de) language pair of the WMT23 biomedical translation task. Our NMT system is built on deep Transformer with larger parameter sizes. Based on the biomedical NMT system trained last year, we leverage Curriculum Learning, Data Diversification, Forward translation, Back translation, and Transductive Ensemble Learning to further improve system performance. Overall, we believe our submission can achieve highly competitive result in the official final evaluation.
%R 10.18653/v1/2023.wmt-1.27
%U https://aclanthology.org/2023.wmt-1.27
%U https://doi.org/10.18653/v1/2023.wmt-1.27
%P 271-274
Markdown (Informal)
[The Path to Continuous Domain Adaptation Improvements by HW-TSC for the WMT23 Biomedical Translation Shared Task](https://aclanthology.org/2023.wmt-1.27) (Wu et al., WMT 2023)
ACL
- Zhanglin Wu, Daimeng Wei, Zongyao Li, Zhengzhe Yu, Shaojun Li, Xiaoyu Chen, Hengchao Shang, Jiaxin Guo, Yuhao Xie, Lizhi Lei, Hao Yang, and Yanfei Jiang. 2023. The Path to Continuous Domain Adaptation Improvements by HW-TSC for the WMT23 Biomedical Translation Shared Task. In Proceedings of the Eighth Conference on Machine Translation, pages 271–274, Singapore. Association for Computational Linguistics.