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
Venues:
EMNLP | 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)
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PDF:
https://aclanthology.org/2021.wmt-1.100.pdf