QEMind: Alibaba’s Submission to the WMT21 Quality Estimation Shared Task
Jiayi Wang, Ke Wang, Boxing Chen, Yu Zhao, Weihua Luo, Yuqi Zhang
Abstract
Quality Estimation, as a crucial step of quality control for machine translation, has been explored for years. The goal is to to investigate automatic methods for estimating the quality of machine translation results without reference translations. In this year’s WMT QE shared task, we utilize the large-scale XLM-Roberta pre-trained model and additionally propose several useful features to evaluate the uncertainty of the translations to build our QE system, named QEMind . The system has been applied to the sentence-level scoring task of Direct Assessment and the binary score prediction task of Critical Error Detection. In this paper, we present our submissions to the WMT 2021 QE shared task and an extensive set of experimental results have shown us that our multilingual systems outperform the best system in the Direct Assessment QE task of WMT 2020.- Anthology ID:
- 2021.wmt-1.100
- 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:
- 948–954
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.100
- DOI:
- Bibkey:
- Cite (ACL):
- Jiayi Wang, Ke Wang, Boxing Chen, Yu Zhao, Weihua Luo, and Yuqi Zhang. 2021. QEMind: Alibaba’s Submission to the WMT21 Quality Estimation Shared Task. In Proceedings of the Sixth Conference on Machine Translation, pages 948–954, Online. Association for Computational Linguistics.
- Cite (Informal):
- QEMind: Alibaba’s Submission to the WMT21 Quality Estimation Shared Task (Wang et al., WMT 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.wmt-1.100.pdf
Export citation
@inproceedings{wang-etal-2021-qemind,
title = "{QEM}ind: {A}libaba{'}s Submission to the {WMT}21 Quality Estimation Shared Task",
author = "Wang, Jiayi and
Wang, Ke and
Chen, Boxing and
Zhao, Yu and
Luo, Weihua and
Zhang, Yuqi",
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-1.100",
pages = "948--954",
abstract = "Quality Estimation, as a crucial step of quality control for machine translation, has been explored for years. The goal is to to investigate automatic methods for estimating the quality of machine translation results without reference translations. In this year{'}s WMT QE shared task, we utilize the large-scale XLM-Roberta pre-trained model and additionally propose several useful features to evaluate the uncertainty of the translations to build our QE system, named \textit{ \textbf{QEMind} }. The system has been applied to the sentence-level scoring task of Direct Assessment and the binary score prediction task of Critical Error Detection. In this paper, we present our submissions to the WMT 2021 QE shared task and an extensive set of experimental results have shown us that our multilingual systems outperform the best system in the Direct Assessment QE task of WMT 2020.",
}
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%0 Conference Proceedings %T QEMind: Alibaba’s Submission to the WMT21 Quality Estimation Shared Task %A Wang, Jiayi %A Wang, Ke %A Chen, Boxing %A Zhao, Yu %A Luo, Weihua %A Zhang, Yuqi %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 wang-etal-2021-qemind %X Quality Estimation, as a crucial step of quality control for machine translation, has been explored for years. The goal is to to investigate automatic methods for estimating the quality of machine translation results without reference translations. In this year’s WMT QE shared task, we utilize the large-scale XLM-Roberta pre-trained model and additionally propose several useful features to evaluate the uncertainty of the translations to build our QE system, named QEMind . The system has been applied to the sentence-level scoring task of Direct Assessment and the binary score prediction task of Critical Error Detection. In this paper, we present our submissions to the WMT 2021 QE shared task and an extensive set of experimental results have shown us that our multilingual systems outperform the best system in the Direct Assessment QE task of WMT 2020. %U https://aclanthology.org/2021.wmt-1.100 %P 948-954
Markdown (Informal)
[QEMind: Alibaba’s Submission to the WMT21 Quality Estimation Shared Task](https://aclanthology.org/2021.wmt-1.100) (Wang et al., WMT 2021)
- QEMind: Alibaba’s Submission to the WMT21 Quality Estimation Shared Task (Wang et al., WMT 2021)
ACL
- Jiayi Wang, Ke Wang, Boxing Chen, Yu Zhao, Weihua Luo, and Yuqi Zhang. 2021. QEMind: Alibaba’s Submission to the WMT21 Quality Estimation Shared Task. In Proceedings of the Sixth Conference on Machine Translation, pages 948–954, Online. Association for Computational Linguistics.