HW-TSC’s Submissions to the WMT22 Word-Level Auto Completion Task
Hao Yang, Hengchao Shang, Zongyao Li, Daimeng Wei, Xianghui He, Xiaoyu Chen, Zhengzhe Yu, Jiaxin Guo, Jinlong Yang, Shaojun Li, Yuanchang Luo, Yuhao Xie, Lizhi Lei, Ying Qin
Abstract
This paper presents the submissions of Huawei Translation Services Center (HW-TSC) to WMT 2022 Word-Level AutoCompletion Task. We propose an end-to-end autoregressive model with bi-context based on Transformer to solve current task. The model uses a mixture of subword and character encoding units to realize the joint encoding of human input, the context of the target side and the decoded sequence, which ensures full utilization of information. We uses one model to solve four types of data structures in the task. During training, we try using a machine translation model as the pre-trained model and fine-tune it for the task. We also add BERT-style MLM data at the fine-tuning stage to improve model performance. We participate in zh→en, en→de, and de→en directions and win the first place in all the three tracks. Particularly, we outperform the second place by more than 5% in terms of accuracy on the zh→en and en→de tracks. The result is buttressed by human evaluations as well, demonstrating the effectiveness of our model.- Anthology ID:
- 2022.wmt-1.122
- 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:
- 1192–1197
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.122
- DOI:
- Bibkey:
- Cite (ACL):
- Hao Yang, Hengchao Shang, Zongyao Li, Daimeng Wei, Xianghui He, Xiaoyu Chen, Zhengzhe Yu, Jiaxin Guo, Jinlong Yang, Shaojun Li, Yuanchang Luo, Yuhao Xie, Lizhi Lei, and Ying Qin. 2022. HW-TSC’s Submissions to the WMT22 Word-Level Auto Completion Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 1192–1197, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- HW-TSC’s Submissions to the WMT22 Word-Level Auto Completion Task (Yang et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.122.pdf
Export citation
@inproceedings{yang-etal-2022-hw-tscs,
title = "{HW}-{TSC}{'}s Submissions to the {WMT}22 Word-Level Auto Completion Task",
author = "Yang, Hao and
Shang, Hengchao and
Li, Zongyao and
Wei, Daimeng and
He, Xianghui and
Chen, Xiaoyu and
Yu, Zhengzhe and
Guo, Jiaxin and
Yang, Jinlong and
Li, Shaojun and
Luo, Yuanchang and
Xie, Yuhao and
Lei, Lizhi 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-1.122",
pages = "1192--1197",
abstract = "This paper presents the submissions of Huawei Translation Services Center (HW-TSC) to WMT 2022 Word-Level AutoCompletion Task. We propose an end-to-end autoregressive model with bi-context based on Transformer to solve current task. The model uses a mixture of subword and character encoding units to realize the joint encoding of human input, the context of the target side and the decoded sequence, which ensures full utilization of information. We uses one model to solve four types of data structures in the task. During training, we try using a machine translation model as the pre-trained model and fine-tune it for the task. We also add BERT-style MLM data at the fine-tuning stage to improve model performance. We participate in zh$\rightarrow$en, en$\rightarrow$de, and de$\rightarrow$en directions and win the first place in all the three tracks. Particularly, we outperform the second place by more than 5{\%} in terms of accuracy on the zh$\rightarrow$en and en$\rightarrow$de tracks. The result is buttressed by human evaluations as well, demonstrating the effectiveness of our model.",
}
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%0 Conference Proceedings %T HW-TSC’s Submissions to the WMT22 Word-Level Auto Completion Task %A Yang, Hao %A Shang, Hengchao %A Li, Zongyao %A Wei, Daimeng %A He, Xianghui %A Chen, Xiaoyu %A Yu, Zhengzhe %A Guo, Jiaxin %A Yang, Jinlong %A Li, Shaojun %A Luo, Yuanchang %A Xie, Yuhao %A Lei, Lizhi %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 yang-etal-2022-hw-tscs %X This paper presents the submissions of Huawei Translation Services Center (HW-TSC) to WMT 2022 Word-Level AutoCompletion Task. We propose an end-to-end autoregressive model with bi-context based on Transformer to solve current task. The model uses a mixture of subword and character encoding units to realize the joint encoding of human input, the context of the target side and the decoded sequence, which ensures full utilization of information. We uses one model to solve four types of data structures in the task. During training, we try using a machine translation model as the pre-trained model and fine-tune it for the task. We also add BERT-style MLM data at the fine-tuning stage to improve model performance. We participate in zh\rightarrowen, en\rightarrowde, and de\rightarrowen directions and win the first place in all the three tracks. Particularly, we outperform the second place by more than 5% in terms of accuracy on the zh\rightarrowen and en\rightarrowde tracks. The result is buttressed by human evaluations as well, demonstrating the effectiveness of our model. %U https://aclanthology.org/2022.wmt-1.122 %P 1192-1197
Markdown (Informal)
[HW-TSC’s Submissions to the WMT22 Word-Level Auto Completion Task](https://aclanthology.org/2022.wmt-1.122) (Yang et al., WMT 2022)
- HW-TSC’s Submissions to the WMT22 Word-Level Auto Completion Task (Yang et al., WMT 2022)
ACL
- Hao Yang, Hengchao Shang, Zongyao Li, Daimeng Wei, Xianghui He, Xiaoyu Chen, Zhengzhe Yu, Jiaxin Guo, Jinlong Yang, Shaojun Li, Yuanchang Luo, Yuhao Xie, Lizhi Lei, and Ying Qin. 2022. HW-TSC’s Submissions to the WMT22 Word-Level Auto Completion Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 1192–1197, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.