@inproceedings{li-etal-2021-hw,
title = "{HW}-{TSC}{'}s Participation in the {WMT} 2021 Triangular {MT} Shared Task",
author = "Li, Zongyao and
Wei, Daimeng and
Shang, Hengchao and
Chen, Xiaoyu and
Wu, Zhanglin and
Yu, Zhengzhe and
Guo, Jiaxin and
Wang, Minghan and
Lei, Lizhi and
Zhang, Min and
Yang, Hao and
Qin, Ying",
booktitle = "Proceedings of the Sixth Conference on Machine Translation",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wmt-1.37",
pages = "325--330",
abstract = "This paper presents the submission of Huawei Translation Service Center (HW-TSC) to WMT 2021 Triangular MT Shared Task. We participate in the Russian-to-Chinese task under the constrained condition. We use Transformer architecture and obtain the best performance via a variant with larger parameter sizes. We perform detailed data pre-processing and filtering on the provided large-scale bilingual data. Several strategies are used to train our models, such as Multilingual Translation, Back Translation, Forward Translation, Data Denoising, Average Checkpoint, Ensemble, Fine-tuning, etc. Our system obtains 32.5 BLEU on the dev set and 27.7 BLEU on the test set, the highest score among all submissions.",
}
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<abstract>This paper presents the submission of Huawei Translation Service Center (HW-TSC) to WMT 2021 Triangular MT Shared Task. We participate in the Russian-to-Chinese task under the constrained condition. We use Transformer architecture and obtain the best performance via a variant with larger parameter sizes. We perform detailed data pre-processing and filtering on the provided large-scale bilingual data. Several strategies are used to train our models, such as Multilingual Translation, Back Translation, Forward Translation, Data Denoising, Average Checkpoint, Ensemble, Fine-tuning, etc. Our system obtains 32.5 BLEU on the dev set and 27.7 BLEU on the test set, the highest score among all submissions.</abstract>
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%0 Conference Proceedings
%T HW-TSC’s Participation in the WMT 2021 Triangular MT Shared Task
%A Li, Zongyao
%A Wei, Daimeng
%A Shang, Hengchao
%A Chen, Xiaoyu
%A Wu, Zhanglin
%A Yu, Zhengzhe
%A Guo, Jiaxin
%A Wang, Minghan
%A Lei, Lizhi
%A Zhang, Min
%A Yang, Hao
%A Qin, Ying
%S Proceedings of the Sixth Conference on Machine Translation
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F li-etal-2021-hw
%X This paper presents the submission of Huawei Translation Service Center (HW-TSC) to WMT 2021 Triangular MT Shared Task. We participate in the Russian-to-Chinese task under the constrained condition. We use Transformer architecture and obtain the best performance via a variant with larger parameter sizes. We perform detailed data pre-processing and filtering on the provided large-scale bilingual data. Several strategies are used to train our models, such as Multilingual Translation, Back Translation, Forward Translation, Data Denoising, Average Checkpoint, Ensemble, Fine-tuning, etc. Our system obtains 32.5 BLEU on the dev set and 27.7 BLEU on the test set, the highest score among all submissions.
%U https://aclanthology.org/2021.wmt-1.37
%P 325-330
Markdown (Informal)
[HW-TSC’s Participation in the WMT 2021 Triangular MT Shared Task](https://aclanthology.org/2021.wmt-1.37) (Li et al., WMT 2021)
ACL
- Zongyao Li, Daimeng Wei, Hengchao Shang, Xiaoyu Chen, Zhanglin Wu, Zhengzhe Yu, Jiaxin Guo, Minghan Wang, Lizhi Lei, Min Zhang, Hao Yang, and Ying Qin. 2021. HW-TSC’s Participation in the WMT 2021 Triangular MT Shared Task. In Proceedings of the Sixth Conference on Machine Translation, pages 325–330, Online. Association for Computational Linguistics.