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

Author

Listed:
  • Martin Popel

    (Charles University)

  • Marketa Tomkova

    (University of Oxford)

  • Jakub Tomek

    (University of Oxford)

  • Łukasz Kaiser

    (Google Brain, Mountain View)

  • Jakob Uszkoreit

    (Google Brain, Mountain View)

  • Ondřej Bojar

    (Charles University)

  • Zdeněk Žabokrtský

    (Charles University)

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.

Suggested Citation

  • Martin Popel & Marketa Tomkova & Jakub Tomek & Łukasz Kaiser & Jakob Uszkoreit & Ondřej Bojar & Zdeněk Žabokrtský, 2020. "Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18073-9
    DOI: 10.1038/s41467-020-18073-9
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    Cited by:

    1. Guo, Zixi & Zhao, Jinzhou & You, Zhenjiang & Li, Yongming & Zhang, Shu & Chen, Yiyu, 2021. "Prediction of coalbed methane production based on deep learning," Energy, Elsevier, vol. 230(C).
    2. Liu, Bokai & Wang, Yizheng & Rabczuk, Timon & Olofsson, Thomas & Lu, Weizhuo, 2024. "Multi-scale modeling in thermal conductivity of Polyurethane incorporated with Phase Change Materials using Physics-Informed Neural Networks," Renewable Energy, Elsevier, vol. 220(C).
    3. Yang, Ying & Zhang, Wei & Lin, Hongyi & Liu, Yang & Qu, Xiaobo, 2024. "Applying masked language model for transport mode choice behavior prediction," Transportation Research Part A: Policy and Practice, Elsevier, vol. 184(C).
    4. Dandan Qiao & Huaxia Rui & Qian Xiong, 2023. "AI and Jobs: Has the Inflection Point Arrived? Evidence from an Online Labor Platform," Papers 2312.04180, arXiv.org, revised Aug 2024.

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