Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation

Ke Wang, Yangbin Shi, Jiayi Wang, Yuqi Zhang, Yu Zhao, Xiaolin Zheng


Abstract
Quality Estimation (QE) plays an essential role in applications of Machine Translation (MT). Traditionally, a QE system accepts the original source text and translation from a black-box MT system as input. Recently, a few studies indicate that as a by-product of translation, QE benefits from the model and training data’s information of the MT system where the translations come from, and it is called the “glass-box QE”. In this paper, we extend the definition of “glass-box QE” generally to uncertainty quantification with both “black-box” and “glass-box” approaches and design several features deduced from them to blaze a new trial in improving QE’s performance. We propose a framework to fuse the feature engineering of uncertainty quantification into a pre-trained cross-lingual language model to predict the translation quality. Experiment results show that our method achieves state-of-the-art performances on the datasets of WMT 2020 QE shared task.
Anthology ID:
2021.findings-emnlp.401
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4687–4698
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.401
DOI:
10.18653/v1/2021.findings-emnlp.401
Bibkey:
Cite (ACL):
Ke Wang, Yangbin Shi, Jiayi Wang, Yuqi Zhang, Yu Zhao, and Xiaolin Zheng. 2021. Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4687–4698, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation (Wang et al., Findings 2021)
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PDF:
https://aclanthology.org/2021.findings-emnlp.401.pdf