@inproceedings{wang-etal-2021-beyond-glass,
title = "Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation",
author = "Wang, Ke and
Shi, Yangbin and
Wang, Jiayi and
Zhang, Yuqi and
Zhao, Yu and
Zheng, Xiaolin",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.401",
doi = "10.18653/v1/2021.findings-emnlp.401",
pages = "4687--4698",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation
%A Wang, Ke
%A Shi, Yangbin
%A Wang, Jiayi
%A Zhang, Yuqi
%A Zhao, Yu
%A Zheng, Xiaolin
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F wang-etal-2021-beyond-glass
%X 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.
%R 10.18653/v1/2021.findings-emnlp.401
%U https://aclanthology.org/2021.findings-emnlp.401
%U https://doi.org/10.18653/v1/2021.findings-emnlp.401
%P 4687-4698
Markdown (Informal)
[Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation](https://aclanthology.org/2021.findings-emnlp.401) (Wang et al., Findings 2021)
ACL