@inproceedings{wu-etal-2023-treating,
title = "Treating General {MT} Shared Task as a Multi-Domain Adaptation Problem: {HW}-{TSC}{'}s Submission to the {WMT}23 General {MT} 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-1.16",
doi = "10.18653/v1/2023.wmt-1.16",
pages = "170--174",
abstract = "This paper presents the submission of Huawei Translate Services Center (HW-TSC) to the WMT23 general machine translation (MT) shared task, in which we participate in Chinese↔English (zh↔en) language pair. We use Transformer architecture and obtain the best performance via a variant with larger parameter size. We perform fine-grained pre-processing and filtering on the provided large-scale bilingual and monolingual datasets. We mainly use model enhancement strategies, including Regularized Dropout, Bidirectional Training, Data Diversification, Forward Translation, Back Translation, Alternated Training, Curriculum Learning and Transductive Ensemble Learning. Our submissions obtain competitive results in the final evaluation.",
}
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<abstract>This paper presents the submission of Huawei Translate Services Center (HW-TSC) to the WMT23 general machine translation (MT) shared task, in which we participate in Chinese↔English (zh↔en) language pair. We use Transformer architecture and obtain the best performance via a variant with larger parameter size. We perform fine-grained pre-processing and filtering on the provided large-scale bilingual and monolingual datasets. We mainly use model enhancement strategies, including Regularized Dropout, Bidirectional Training, Data Diversification, Forward Translation, Back Translation, Alternated Training, Curriculum Learning and Transductive Ensemble Learning. Our submissions obtain competitive results in the final evaluation.</abstract>
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%0 Conference Proceedings
%T Treating General MT Shared Task as a Multi-Domain Adaptation Problem: HW-TSC’s Submission to the WMT23 General MT 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-treating
%X This paper presents the submission of Huawei Translate Services Center (HW-TSC) to the WMT23 general machine translation (MT) shared task, in which we participate in Chinese↔English (zh↔en) language pair. We use Transformer architecture and obtain the best performance via a variant with larger parameter size. We perform fine-grained pre-processing and filtering on the provided large-scale bilingual and monolingual datasets. We mainly use model enhancement strategies, including Regularized Dropout, Bidirectional Training, Data Diversification, Forward Translation, Back Translation, Alternated Training, Curriculum Learning and Transductive Ensemble Learning. Our submissions obtain competitive results in the final evaluation.
%R 10.18653/v1/2023.wmt-1.16
%U https://aclanthology.org/2023.wmt-1.16
%U https://doi.org/10.18653/v1/2023.wmt-1.16
%P 170-174
Markdown (Informal)
[Treating General MT Shared Task as a Multi-Domain Adaptation Problem: HW-TSC’s Submission to the WMT23 General MT Shared Task](https://aclanthology.org/2023.wmt-1.16) (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. Treating General MT Shared Task as a Multi-Domain Adaptation Problem: HW-TSC’s Submission to the WMT23 General MT Shared Task. In Proceedings of the Eighth Conference on Machine Translation, pages 170–174, Singapore. Association for Computational Linguistics.