Journal article
Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals.
- Abstract:
- The quality of human translation was long thought to be unattainable for computer translation systems. In this study, we present a deep-learning system, CUBBITT, which challenges this view. In a context-aware blind evaluation by human judges, CUBBITT significantly outperformed professional-agency English-to-Czech news translation in preserving text meaning (translation adequacy). While human translation is still rated as more fluent, CUBBITT is shown to be substantially more fluent than previous state-of-the-art systems. Moreover, most participants of a Translation Turing test struggle to distinguish CUBBITT translations from human translations. This work approaches the quality of human translation and even surpasses it in adequacy in certain circumstances.This suggests that deep learning may have the potential to replace humans in applications where conservation of meaning is the primary aim.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 2.5MB, Terms of use)
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- Publisher copy:
- 10.1038/s41467-020-18073-9
Authors
- Publisher:
- Springer Nature
- Journal:
- Nature Communications More from this journal
- Volume:
- 11
- Issue:
- 1
- Article number:
- 4381
- Publication date:
- 2020-09-01
- Acceptance date:
- 2020-07-24
- DOI:
- EISSN:
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2041-1723
- Pmid:
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32873773
- Language:
-
English
- Keywords:
- Pubs id:
-
1130814
- Local pid:
-
pubs:1130814
- Deposit date:
-
2020-12-17
- ARK identifier:
Terms of use
- Copyright holder:
- Popel et al.
- Copyright date:
- 2020
- Rights statement:
- ©2020 The Author(s).
- Notes:
- Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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