@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",
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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<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.</abstract>
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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
%S Proceedings of the Sixth Conference on Machine Translation
%D 2021
%8 nov
%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)
ACL