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        <title>Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation</title>
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    <name type="personal">
        <namePart type="given">Ke</namePart>
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    <name type="personal">
        <namePart type="given">Jiayi</namePart>
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    <name type="personal">
        <namePart type="given">Yuqi</namePart>
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    <name type="personal">
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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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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.401</identifier>
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        <date>2021-nov</date>
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            <start>4687</start>
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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
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 nov
%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)

• Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation (Wang et al., Findings 2021)

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.