HW-TSC’s Participation in the WMT 2021 Efficiency Shared Task
Hengchao Shang, Ting Hu, Daimeng Wei, Zongyao Li, Jianfei Feng, ZhengZhe Yu, Jiaxin Guo, Shaojun Li, Lizhi Lei, ShiMin Tao, Hao Yang, Jun Yao, Ying Qin
Abstract
This paper presents the submission of Huawei Translation Services Center (HW-TSC) to WMT 2021 Efficiency Shared Task. We explore the sentence-level teacher-student distillation technique and train several small-size models that find a balance between efficiency and quality. Our models feature deep encoder, shallow decoder and light-weight RNN with SSRU layer. We use Huawei Noah’s Bolt, an efficient and light-weight library for on-device inference. Leveraging INT8 quantization, self-defined General Matrix Multiplication (GEMM) operator, shortlist, greedy search and caching, we submit four small-size and efficient translation models with high translation quality for the one CPU core latency track.- Anthology ID:
- 2021.wmt-1.75
- Volume:
- Proceedings of the Sixth Conference on Machine Translation
- Month:
- November
- Year:
- 2021
- Address:
- Online
- Editors:
- Loic Barrault, Ondrej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussa, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Tom Kocmi, Andre Martins, Makoto Morishita, Christof Monz
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 781–786
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.75
- DOI:
- Bibkey:
- Cite (ACL):
- Hengchao Shang, Ting Hu, Daimeng Wei, Zongyao Li, Jianfei Feng, ZhengZhe Yu, Jiaxin Guo, Shaojun Li, Lizhi Lei, ShiMin Tao, Hao Yang, Jun Yao, and Ying Qin. 2021. HW-TSC’s Participation in the WMT 2021 Efficiency Shared Task. In Proceedings of the Sixth Conference on Machine Translation, pages 781–786, Online. Association for Computational Linguistics.
- Cite (Informal):
- HW-TSC’s Participation in the WMT 2021 Efficiency Shared Task (Shang et al., WMT 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.wmt-1.75.pdf
Export citation
@inproceedings{shang-etal-2021-hw,
title = "{HW}-{TSC}{'}s Participation in the {WMT} 2021 Efficiency Shared Task",
author = "Shang, Hengchao and
Hu, Ting and
Wei, Daimeng and
Li, Zongyao and
Feng, Jianfei and
Yu, ZhengZhe and
Guo, Jiaxin and
Li, Shaojun and
Lei, Lizhi and
Tao, ShiMin and
Yang, Hao and
Yao, Jun and
Qin, Ying",
editor = "Barrault, Loic and
Bojar, Ondrej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-jussa, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Kocmi, Tom and
Martins, Andre and
Morishita, Makoto and
Monz, Christof",
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-1.75",
pages = "781--786",
abstract = "This paper presents the submission of Huawei Translation Services Center (HW-TSC) to WMT 2021 Efficiency Shared Task. We explore the sentence-level teacher-student distillation technique and train several small-size models that find a balance between efficiency and quality. Our models feature deep encoder, shallow decoder and light-weight RNN with SSRU layer. We use Huawei Noah{'}s Bolt, an efficient and light-weight library for on-device inference. Leveraging INT8 quantization, self-defined General Matrix Multiplication (GEMM) operator, shortlist, greedy search and caching, we submit four small-size and efficient translation models with high translation quality for the one CPU core latency track.",
}
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%0 Conference Proceedings %T HW-TSC’s Participation in the WMT 2021 Efficiency Shared Task %A Shang, Hengchao %A Hu, Ting %A Wei, Daimeng %A Li, Zongyao %A Feng, Jianfei %A Yu, ZhengZhe %A Guo, Jiaxin %A Li, Shaojun %A Lei, Lizhi %A Tao, ShiMin %A Yang, Hao %A Yao, Jun %A Qin, Ying %Y Barrault, Loic %Y Bojar, Ondrej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussa, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Freitag, Markus %Y Graham, Yvette %Y Grundkiewicz, Roman %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Kocmi, Tom %Y Martins, Andre %Y Morishita, Makoto %Y Monz, Christof %S Proceedings of the Sixth Conference on Machine Translation %D 2021 %8 November %I Association for Computational Linguistics %C Online %F shang-etal-2021-hw %X This paper presents the submission of Huawei Translation Services Center (HW-TSC) to WMT 2021 Efficiency Shared Task. We explore the sentence-level teacher-student distillation technique and train several small-size models that find a balance between efficiency and quality. Our models feature deep encoder, shallow decoder and light-weight RNN with SSRU layer. We use Huawei Noah’s Bolt, an efficient and light-weight library for on-device inference. Leveraging INT8 quantization, self-defined General Matrix Multiplication (GEMM) operator, shortlist, greedy search and caching, we submit four small-size and efficient translation models with high translation quality for the one CPU core latency track. %U https://aclanthology.org/2021.wmt-1.75 %P 781-786
Markdown (Informal)
[HW-TSC’s Participation in the WMT 2021 Efficiency Shared Task](https://aclanthology.org/2021.wmt-1.75) (Shang et al., WMT 2021)
- HW-TSC’s Participation in the WMT 2021 Efficiency Shared Task (Shang et al., WMT 2021)
ACL
- Hengchao Shang, Ting Hu, Daimeng Wei, Zongyao Li, Jianfei Feng, ZhengZhe Yu, Jiaxin Guo, Shaojun Li, Lizhi Lei, ShiMin Tao, Hao Yang, Jun Yao, and Ying Qin. 2021. HW-TSC’s Participation in the WMT 2021 Efficiency Shared Task. In Proceedings of the Sixth Conference on Machine Translation, pages 781–786, Online. Association for Computational Linguistics.