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Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10424334" target="_blank" >RIV/00216208:11320/20:10424334 - isvavai.cz</a>

  • Result on the web

    <a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=0k1mY-gfTl" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=0k1mY-gfTl</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1038/s41467-020-18073-9" target="_blank" >10.1038/s41467-020-18073-9</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Nature Communications

  • ISSN

    2041-1723

  • e-ISSN

  • Volume of the periodical

    11

  • Issue of the periodical within the volume

    4381

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    15

  • Pages from-to

    1-15

  • UT code for WoS article

    000569891500008

  • EID of the result in the Scopus database

    2-s2.0-85090052524