HW-TSC’s Submission for the WMT22 Efficiency Task
Hengchao Shang, Ting Hu, Daimeng Wei, Zongyao Li, Xianzhi Yu, Jianfei Feng, Ting Zhu, Lizhi Lei, Shimin Tao, Hao Yang, Ying Qin, Jinlong Yang, Zhiqiang Rao, Zhengzhe Yu
Abstract
This paper presents the submission of Huawei Translation Services Center (HW-TSC) to WMT 2022 Efficiency Shared Task. For this year’s task, we still apply sentence-level distillation strategy to train small models with different configurations. Then, we integrate the average attention mechanism into the lightweight RNN model to pursue more efficient decoding. We tried adding a retrain step to our 8-bit and 4-bit models to achieve a balance between model size and quality. We still use Huawei Noah’s Bolt for INT8 inference and 4-bit storage. Coupled with Bolt’s support for batch inference and multi-core parallel computing, we finally submit models with different configurations to the CPU latency and throughput tracks to explore the Pareto frontiers.- Anthology ID:
- 2022.wmt-1.66
- Volume:
- Proceedings of the Seventh Conference on Machine Translation (WMT)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Marco Turchi, Marcos Zampieri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 677–681
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.66
- DOI:
- Bibkey:
- Cite (ACL):
- Hengchao Shang, Ting Hu, Daimeng Wei, Zongyao Li, Xianzhi Yu, Jianfei Feng, Ting Zhu, Lizhi Lei, Shimin Tao, Hao Yang, Ying Qin, Jinlong Yang, Zhiqiang Rao, and Zhengzhe Yu. 2022. HW-TSC’s Submission for the WMT22 Efficiency Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 677–681, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- HW-TSC’s Submission for the WMT22 Efficiency Task (Shang et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.66.pdf
Export citation
@inproceedings{shang-etal-2022-hw,
title = "{HW}-{TSC}{'}s Submission for the {WMT}22 Efficiency Task",
author = "Shang, Hengchao and
Hu, Ting and
Wei, Daimeng and
Li, Zongyao and
Yu, Xianzhi and
Feng, Jianfei and
Zhu, Ting and
Lei, Lizhi and
Tao, Shimin and
Yang, Hao and
Qin, Ying and
Yang, Jinlong and
Rao, Zhiqiang and
Yu, Zhengzhe",
editor = {Koehn, Philipp and
Barrault, Lo{\"\i}c and
Bojar, Ond{\v{r}}ej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss{\`a}, 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
Jimeno Yepes, Antonio and
Kocmi, Tom and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Popel, Martin and
Turchi, Marco and
Zampieri, Marcos},
booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wmt-1.66",
pages = "677--681",
abstract = "This paper presents the submission of Huawei Translation Services Center (HW-TSC) to WMT 2022 Efficiency Shared Task. For this year{'}s task, we still apply sentence-level distillation strategy to train small models with different configurations. Then, we integrate the average attention mechanism into the lightweight RNN model to pursue more efficient decoding. We tried adding a retrain step to our 8-bit and 4-bit models to achieve a balance between model size and quality. We still use Huawei Noah{'}s Bolt for INT8 inference and 4-bit storage. Coupled with Bolt{'}s support for batch inference and multi-core parallel computing, we finally submit models with different configurations to the CPU latency and throughput tracks to explore the Pareto frontiers.",
}
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<abstract>This paper presents the submission of Huawei Translation Services Center (HW-TSC) to WMT 2022 Efficiency Shared Task. For this year’s task, we still apply sentence-level distillation strategy to train small models with different configurations. Then, we integrate the average attention mechanism into the lightweight RNN model to pursue more efficient decoding. We tried adding a retrain step to our 8-bit and 4-bit models to achieve a balance between model size and quality. We still use Huawei Noah’s Bolt for INT8 inference and 4-bit storage. Coupled with Bolt’s support for batch inference and multi-core parallel computing, we finally submit models with different configurations to the CPU latency and throughput tracks to explore the Pareto frontiers.</abstract>
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%0 Conference Proceedings %T HW-TSC’s Submission for the WMT22 Efficiency Task %A Shang, Hengchao %A Hu, Ting %A Wei, Daimeng %A Li, Zongyao %A Yu, Xianzhi %A Feng, Jianfei %A Zhu, Ting %A Lei, Lizhi %A Tao, Shimin %A Yang, Hao %A Qin, Ying %A Yang, Jinlong %A Rao, Zhiqiang %A Yu, Zhengzhe %Y Koehn, Philipp %Y Barrault, Loïc %Y Bojar, Ondřej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussà, 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 Jimeno Yepes, Antonio %Y Kocmi, Tom %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Popel, Martin %Y Turchi, Marco %Y Zampieri, Marcos %S Proceedings of the Seventh Conference on Machine Translation (WMT) %D 2022 %8 December %I Association for Computational Linguistics %C Abu Dhabi, United Arab Emirates (Hybrid) %F shang-etal-2022-hw %X This paper presents the submission of Huawei Translation Services Center (HW-TSC) to WMT 2022 Efficiency Shared Task. For this year’s task, we still apply sentence-level distillation strategy to train small models with different configurations. Then, we integrate the average attention mechanism into the lightweight RNN model to pursue more efficient decoding. We tried adding a retrain step to our 8-bit and 4-bit models to achieve a balance between model size and quality. We still use Huawei Noah’s Bolt for INT8 inference and 4-bit storage. Coupled with Bolt’s support for batch inference and multi-core parallel computing, we finally submit models with different configurations to the CPU latency and throughput tracks to explore the Pareto frontiers. %U https://aclanthology.org/2022.wmt-1.66 %P 677-681
Markdown (Informal)
[HW-TSC’s Submission for the WMT22 Efficiency Task](https://aclanthology.org/2022.wmt-1.66) (Shang et al., WMT 2022)
- HW-TSC’s Submission for the WMT22 Efficiency Task (Shang et al., WMT 2022)
ACL
- Hengchao Shang, Ting Hu, Daimeng Wei, Zongyao Li, Xianzhi Yu, Jianfei Feng, Ting Zhu, Lizhi Lei, Shimin Tao, Hao Yang, Ying Qin, Jinlong Yang, Zhiqiang Rao, and Zhengzhe Yu. 2022. HW-TSC’s Submission for the WMT22 Efficiency Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 677–681, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.