The NiuTrans Machine Translation Systems for WMT22
Weiqiao Shan, Zhiquan Cao, Yuchen Han, Siming Wu, Yimin Hu, Jie Wang, Yi Zhang, Hou Baoyu, Hang Cao, Chenghao Gao, Xiaowen Liu, Tong Xiao, Anxiang Ma, Jingbo Zhu
Abstract
This paper describes the NiuTrans neural machine translation systems of the WMT22 General MT constrained task. We participate in four directions, including Chinese→English, English→Croatian, and Livonian↔English. Our models are based on several advanced Transformer variants, e.g., Transformer-ODE, Universal Multiscale Transformer (UMST). The main workflow consists of data filtering, large-scale data augmentation (i.e., iterative back-translation, iterative knowledge distillation), and specific-domain fine-tuning. Moreover, we try several multi-domain methods, such as a multi-domain model structure and a multi-domain data clustering method, to rise to this year’s newly proposed multi-domain test set challenge. For low-resource scenarios, we build a multi-language translation model to enhance the performance, and try to use the pre-trained language model (mBERT) to initialize the translation model.- Anthology ID:
- 2022.wmt-1.32
- 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:
- 366–374
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.32
- DOI:
- Bibkey:
- Cite (ACL):
- Weiqiao Shan, Zhiquan Cao, Yuchen Han, Siming Wu, Yimin Hu, Jie Wang, Yi Zhang, Hou Baoyu, Hang Cao, Chenghao Gao, Xiaowen Liu, Tong Xiao, Anxiang Ma, and Jingbo Zhu. 2022. The NiuTrans Machine Translation Systems for WMT22. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 366–374, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- The NiuTrans Machine Translation Systems for WMT22 (Shan et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.32.pdf
Export citation
@inproceedings{shan-etal-2022-niutrans,
title = "The {N}iu{T}rans Machine Translation Systems for {WMT}22",
author = "Shan, Weiqiao and
Cao, Zhiquan and
Han, Yuchen and
Wu, Siming and
Hu, Yimin and
Wang, Jie and
Zhang, Yi and
Baoyu, Hou and
Cao, Hang and
Gao, Chenghao and
Liu, Xiaowen and
Xiao, Tong and
Ma, Anxiang and
Zhu, Jingbo",
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-1.32",
pages = "366--374",
abstract = "This paper describes the NiuTrans neural machine translation systems of the WMT22 General MT constrained task. We participate in four directions, including Chinese→English, English→Croatian, and Livonian↔English. Our models are based on several advanced Transformer variants, e.g., Transformer-ODE, Universal Multiscale Transformer (UMST). The main workflow consists of data filtering, large-scale data augmentation (i.e., iterative back-translation, iterative knowledge distillation), and specific-domain fine-tuning. Moreover, we try several multi-domain methods, such as a multi-domain model structure and a multi-domain data clustering method, to rise to this year{'}s newly proposed multi-domain test set challenge. For low-resource scenarios, we build a multi-language translation model to enhance the performance, and try to use the pre-trained language model (mBERT) to initialize the translation model.",
}
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<abstract>This paper describes the NiuTrans neural machine translation systems of the WMT22 General MT constrained task. We participate in four directions, including Chinese→English, English→Croatian, and Livonian↔English. Our models are based on several advanced Transformer variants, e.g., Transformer-ODE, Universal Multiscale Transformer (UMST). The main workflow consists of data filtering, large-scale data augmentation (i.e., iterative back-translation, iterative knowledge distillation), and specific-domain fine-tuning. Moreover, we try several multi-domain methods, such as a multi-domain model structure and a multi-domain data clustering method, to rise to this year’s newly proposed multi-domain test set challenge. For low-resource scenarios, we build a multi-language translation model to enhance the performance, and try to use the pre-trained language model (mBERT) to initialize the translation model.</abstract>
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%0 Conference Proceedings %T The NiuTrans Machine Translation Systems for WMT22 %A Shan, Weiqiao %A Cao, Zhiquan %A Han, Yuchen %A Wu, Siming %A Hu, Yimin %A Wang, Jie %A Zhang, Yi %A Baoyu, Hou %A Cao, Hang %A Gao, Chenghao %A Liu, Xiaowen %A Xiao, Tong %A Ma, Anxiang %A Zhu, Jingbo %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 shan-etal-2022-niutrans %X This paper describes the NiuTrans neural machine translation systems of the WMT22 General MT constrained task. We participate in four directions, including Chinese→English, English→Croatian, and Livonian↔English. Our models are based on several advanced Transformer variants, e.g., Transformer-ODE, Universal Multiscale Transformer (UMST). The main workflow consists of data filtering, large-scale data augmentation (i.e., iterative back-translation, iterative knowledge distillation), and specific-domain fine-tuning. Moreover, we try several multi-domain methods, such as a multi-domain model structure and a multi-domain data clustering method, to rise to this year’s newly proposed multi-domain test set challenge. For low-resource scenarios, we build a multi-language translation model to enhance the performance, and try to use the pre-trained language model (mBERT) to initialize the translation model. %U https://aclanthology.org/2022.wmt-1.32 %P 366-374
Markdown (Informal)
[The NiuTrans Machine Translation Systems for WMT22](https://aclanthology.org/2022.wmt-1.32) (Shan et al., WMT 2022)
- The NiuTrans Machine Translation Systems for WMT22 (Shan et al., WMT 2022)
ACL
- Weiqiao Shan, Zhiquan Cao, Yuchen Han, Siming Wu, Yimin Hu, Jie Wang, Yi Zhang, Hou Baoyu, Hang Cao, Chenghao Gao, Xiaowen Liu, Tong Xiao, Anxiang Ma, and Jingbo Zhu. 2022. The NiuTrans Machine Translation Systems for WMT22. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 366–374, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.