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Unsupervised quality estimation for neural machine translation
Fomicheva, Marina Sun, Shuo Yankovskaya, Lisa Blain, Frédéric Guzmán, Francisco Fishel, Mark Aletras, Nikolaos Chaudhary, Vishrav Specia, Lucia
Fomicheva, Marina
Sun, Shuo
Yankovskaya, Lisa
Blain, Frédéric
Guzmán, Francisco
Fishel, Mark
Aletras, Nikolaos
Chaudhary, Vishrav
Specia, Lucia
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2020-09-01
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Abstract
Quality Estimation (QE) is an important component in making Machine Translation (MT) useful in real-world applications, as it is aimed to inform the user on the quality of the MT output at test time. Existing approaches require large amounts of expert annotated data, computation and time for training. As an alternative, we devise an unsupervised approach to QE where no training or access to additional resources besides the MT system itself is required. Different from most of the current work that treats the MT system as a black box, we explore useful information that can be extracted from the MT system as a by-product of translation. By employing methods for uncertainty quantification, we achieve very good correlation with human judgments of quality, rivalling state-of-the-art supervised QE models. To evaluate our approach we collect the first dataset that enables work on both black-box and glass-box approaches to QE.
Citation
Fomicheva, M., Sun, S., Yankovskaya, L., Blain, F. et al. (2020) Unsupervised quality estimation for neural machine translation, Transactions of the Association for Computational Linguistics, 8, pp. 539-555.
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Journal article
Language
en
Description
© 2020 The Authors. Published by Association for Computational Linguistics. This is an open access article available under a Creative Commons licence.
The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1162/tacl_a_00330
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2307-387X