HW-TSC Translation Systems for the WMT22 Biomedical Translation Task
Zhanglin Wu, Jinlong Yang, Zhiqiang Rao, Zhengzhe Yu, Daimeng Wei, Xiaoyu Chen, Zongyao Li, Hengchao Shang, Shaojun Li, Ming Zhu, Yuanchang Luo, Yuhao Xie, Miaomiao Ma, Ting Zhu, Lizhi Lei, Song Peng, Hao Yang, Ying Qin
Correct Metadata for
Abstract
This paper describes the translation systems trained by Huawei translation services center (HW-TSC) for the WMT22 biomedical translation task in five language pairs: English↔German (en↔de), English↔French (en↔fr), English↔Chinese (en↔zh), English↔Russian (en↔ru) and Spanish→English (es→en). Our primary systems are built on deep Transformer with a large filter size. We also utilize R-Drop, data diversification, forward translation, back translation, data selection, finetuning and ensemble to improve the system performance. According to the official evaluation results in OCELoT or CodaLab, our unconstrained systems in en→de, de→en, en→fr, fr→en, en→zh and es→en (clinical terminology sub-track) get the highest BLEU scores among all submissions for the WMT22 biomedical translation task.- Anthology ID:
- 2022.wmt-1.88
- 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:
- 936–942
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.88/
- DOI:
- 10.18653/v1/2022.wmt-1.88
- Bibkey:
- Cite (ACL):
- Zhanglin Wu, Jinlong Yang, Zhiqiang Rao, Zhengzhe Yu, Daimeng Wei, Xiaoyu Chen, Zongyao Li, Hengchao Shang, Shaojun Li, Ming Zhu, Yuanchang Luo, Yuhao Xie, Miaomiao Ma, Ting Zhu, Lizhi Lei, Song Peng, Hao Yang, and Ying Qin. 2022. HW-TSC Translation Systems for the WMT22 Biomedical Translation Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 936–942, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- HW-TSC Translation Systems for the WMT22 Biomedical Translation Task (Wu et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.88.pdf
Export citation
@inproceedings{wu-etal-2022-hw,
title = "{HW}-{TSC} Translation Systems for the {WMT}22 Biomedical Translation Task",
author = "Wu, Zhanglin and
Yang, Jinlong and
Rao, Zhiqiang and
Yu, Zhengzhe and
Wei, Daimeng and
Chen, Xiaoyu and
Li, Zongyao and
Shang, Hengchao and
Li, Shaojun and
Zhu, Ming and
Luo, Yuanchang and
Xie, Yuhao and
Ma, Miaomiao and
Zhu, Ting and
Lei, Lizhi and
Peng, Song and
Yang, Hao and
Qin, Ying",
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-1.88/",
doi = "10.18653/v1/2022.wmt-1.88",
pages = "936--942",
abstract = "This paper describes the translation systems trained by Huawei translation services center (HW-TSC) for the WMT22 biomedical translation task in five language pairs: English{\ensuremath{\leftrightarrow}}German (en{\ensuremath{\leftrightarrow}}de), English{\ensuremath{\leftrightarrow}}French (en{\ensuremath{\leftrightarrow}}fr), English{\ensuremath{\leftrightarrow}}Chinese (en{\ensuremath{\leftrightarrow}}zh), English{\ensuremath{\leftrightarrow}}Russian (en{\ensuremath{\leftrightarrow}}ru) and Spanish{\textrightarrow}English (es{\textrightarrow}en). Our primary systems are built on deep Transformer with a large filter size. We also utilize R-Drop, data diversification, forward translation, back translation, data selection, finetuning and ensemble to improve the system performance. According to the official evaluation results in OCELoT or CodaLab, our unconstrained systems in en{\textrightarrow}de, de{\textrightarrow}en, en{\textrightarrow}fr, fr{\textrightarrow}en, en{\textrightarrow}zh and es{\textrightarrow}en (clinical terminology sub-track) get the highest BLEU scores among all submissions for the WMT22 biomedical translation task."
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<abstract>This paper describes the translation systems trained by Huawei translation services center (HW-TSC) for the WMT22 biomedical translation task in five language pairs: English\ensuremathłeftrightarrowGerman (en\ensuremathłeftrightarrowde), English\ensuremathłeftrightarrowFrench (en\ensuremathłeftrightarrowfr), English\ensuremathłeftrightarrowChinese (en\ensuremathłeftrightarrowzh), English\ensuremathłeftrightarrowRussian (en\ensuremathłeftrightarrowru) and Spanish→English (es→en). Our primary systems are built on deep Transformer with a large filter size. We also utilize R-Drop, data diversification, forward translation, back translation, data selection, finetuning and ensemble to improve the system performance. According to the official evaluation results in OCELoT or CodaLab, our unconstrained systems in en→de, de→en, en→fr, fr→en, en→zh and es→en (clinical terminology sub-track) get the highest BLEU scores among all submissions for the WMT22 biomedical translation task.</abstract>
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%0 Conference Proceedings %T HW-TSC Translation Systems for the WMT22 Biomedical Translation Task %A Wu, Zhanglin %A Yang, Jinlong %A Rao, Zhiqiang %A Yu, Zhengzhe %A Wei, Daimeng %A Chen, Xiaoyu %A Li, Zongyao %A Shang, Hengchao %A Li, Shaojun %A Zhu, Ming %A Luo, Yuanchang %A Xie, Yuhao %A Ma, Miaomiao %A Zhu, Ting %A Lei, Lizhi %A Peng, Song %A Yang, Hao %A Qin, Ying %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 wu-etal-2022-hw %X This paper describes the translation systems trained by Huawei translation services center (HW-TSC) for the WMT22 biomedical translation task in five language pairs: English\ensuremathłeftrightarrowGerman (en\ensuremathłeftrightarrowde), English\ensuremathłeftrightarrowFrench (en\ensuremathłeftrightarrowfr), English\ensuremathłeftrightarrowChinese (en\ensuremathłeftrightarrowzh), English\ensuremathłeftrightarrowRussian (en\ensuremathłeftrightarrowru) and Spanish→English (es→en). Our primary systems are built on deep Transformer with a large filter size. We also utilize R-Drop, data diversification, forward translation, back translation, data selection, finetuning and ensemble to improve the system performance. According to the official evaluation results in OCELoT or CodaLab, our unconstrained systems in en→de, de→en, en→fr, fr→en, en→zh and es→en (clinical terminology sub-track) get the highest BLEU scores among all submissions for the WMT22 biomedical translation task. %R 10.18653/v1/2022.wmt-1.88 %U https://aclanthology.org/2022.wmt-1.88/ %U https://doi.org/10.18653/v1/2022.wmt-1.88 %P 936-942
Markdown (Informal)
[HW-TSC Translation Systems for the WMT22 Biomedical Translation Task](https://aclanthology.org/2022.wmt-1.88/) (Wu et al., WMT 2022)
- HW-TSC Translation Systems for the WMT22 Biomedical Translation Task (Wu et al., WMT 2022)
ACL
- Zhanglin Wu, Jinlong Yang, Zhiqiang Rao, Zhengzhe Yu, Daimeng Wei, Xiaoyu Chen, Zongyao Li, Hengchao Shang, Shaojun Li, Ming Zhu, Yuanchang Luo, Yuhao Xie, Miaomiao Ma, Ting Zhu, Lizhi Lei, Song Peng, Hao Yang, and Ying Qin. 2022. HW-TSC Translation Systems for the WMT22 Biomedical Translation Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 936–942, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.