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Abstract
This study introduces CUBBITT, a deep-learning system that achieves news translation quality comparable to human professionals. In a blind evaluation, CUBBITT significantly outperformed professional-agency English-to-Czech news translation in preserving text meaning (adequacy). While human translation was rated as more fluent, CUBBITT showed substantially higher fluency than previous state-of-the-art systems. Many participants in a Turing test struggled to distinguish CUBBITT translations from human translations, suggesting deep learning's potential to replace humans in meaning-focused applications.
Publisher
Nature Communications
Published On
Oct 27, 2020
Authors
Martin Popel, Marketa Tomkova, Jakub Tomek, Lukasz Kaiser, Jakob Uszkoreit, Ondřej Bojar, Zdeněk Žabokrtský
Tags
CUBBITT
deep learning
news translation
fluency
adequacy
Turing test
professional translation
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