@inproceedings{wei-etal-2021-hw,
title = "{HW}-{TSC}{'}s Participation in the {WMT} 2021 News Translation Shared Task",
author = "Wei, Daimeng and
Li, Zongyao and
Wu, Zhanglin and
Yu, Zhengzhe and
Chen, Xiaoyu and
Shang, Hengchao 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-1.21",
pages = "225--231",
abstract = "This paper presents the submission of Huawei Translate Services Center (HW-TSC) to the WMT 2021 News Translation Shared Task. We participate in 7 language pairs, including Zh/En, De/En, Ja/En, Ha/En, Is/En, Hi/Bn, and Xh/Zu in both directions under the constrained condition. We use Transformer architecture and obtain the best performance via multiple variants with larger parameter sizes. We perform detailed pre-processing and filtering on the provided large-scale bilingual and monolingual datasets. Several commonly used strategies are used to train our models, such as Back Translation, Forward Translation, Multilingual Translation, Ensemble Knowledge Distillation, etc. Our submission obtains 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 WMT 2021 News Translation Shared Task. We participate in 7 language pairs, including Zh/En, De/En, Ja/En, Ha/En, Is/En, Hi/Bn, and Xh/Zu in both directions under the constrained condition. We use Transformer architecture and obtain the best performance via multiple variants with larger parameter sizes. We perform detailed pre-processing and filtering on the provided large-scale bilingual and monolingual datasets. Several commonly used strategies are used to train our models, such as Back Translation, Forward Translation, Multilingual Translation, Ensemble Knowledge Distillation, etc. Our submission obtains competitive results in the final evaluation.</abstract>
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%0 Conference Proceedings
%T HW-TSC’s Participation in the WMT 2021 News Translation Shared Task
%A Wei, Daimeng
%A Li, Zongyao
%A Wu, Zhanglin
%A Yu, Zhengzhe
%A Chen, Xiaoyu
%A Shang, Hengchao
%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 nov
%I Association for Computational Linguistics
%C Online
%F wei-etal-2021-hw
%X This paper presents the submission of Huawei Translate Services Center (HW-TSC) to the WMT 2021 News Translation Shared Task. We participate in 7 language pairs, including Zh/En, De/En, Ja/En, Ha/En, Is/En, Hi/Bn, and Xh/Zu in both directions under the constrained condition. We use Transformer architecture and obtain the best performance via multiple variants with larger parameter sizes. We perform detailed pre-processing and filtering on the provided large-scale bilingual and monolingual datasets. Several commonly used strategies are used to train our models, such as Back Translation, Forward Translation, Multilingual Translation, Ensemble Knowledge Distillation, etc. Our submission obtains competitive results in the final evaluation.
%U https://aclanthology.org/2021.wmt-1.21
%P 225-231
Markdown (Informal)
[HW-TSC’s Participation in the WMT 2021 News Translation Shared Task](https://aclanthology.org/2021.wmt-1.21) (Wei et al., WMT 2021)
ACL
- Daimeng Wei, Zongyao Li, Zhanglin Wu, Zhengzhe Yu, Xiaoyu Chen, Hengchao Shang, Jiaxin Guo, Minghan Wang, Lizhi Lei, Min Zhang, Hao Yang, and Ying Qin. 2021. HW-TSC’s Participation in the WMT 2021 News Translation Shared Task. In Proceedings of the Sixth Conference on Machine Translation, pages 225–231, Online. Association for Computational Linguistics.