Fomicheva, M., Sun, S., Yankovskaya, L. et al. (6 more authors) (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics, 8 (2020). pp. 539-555. ISSN 2307-387X
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Association for Computational Linguistics. Distributed under a CC-BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 May 2020 10:19 |
Last Modified: | 04 Nov 2020 14:48 |
Status: | Published |
Publisher: | MIT Press |
Refereed: | Yes |
Identification Number: | 10.1162/tacl_a_00330 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:161219 |