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Article published In:
Translation Spaces
Vol. 7:2 (2018) ► pp.240–262

Translators’ perceptions of literary post-editing using statistical and neural machine translation

ORCID logo | Dublin City University
ORCID logo | University of Groningen
ORCID logo | Dublin City University
ORCID logo | Dublin City University

In the context of recent improvements in the quality of machine translation (MT) output and new use cases being found for that output, this article reports on an experiment using statistical and neural MT systems to translate literature. Six professional translators with experience of literary translation produced English-to-Catalan translations under three conditions: translation from scratch, neural MT post-editing, and statistical MT post-editing. They provided feedback before and after the translation via questionnaires and interviews. While all participants prefer to translate from scratch, mostly due to the freedom to be creative without the constraints of segment-level segmentation, those with less experience find the MT suggestions useful.

Keywords: machine translation, literary machine translation, neural machine translation, human factors in machine translation
Article outline
  • Introduction
  • Related literature
    • Perceptions of post-editing
    • Neural MT
    • MT for literature
  • Methodology
    • MT systems and training
    • Test data: Warbreaker
    • Participant profiles
    • Experimental setup
  • Results
    • Measurements and perceptions of post-editing effort
      • Temporal effort
      • Technical effort
    • Users’ perceptions of the task
      • Creativity vs. productivity
      • Segmentation and context
      • Source vs. target (adequacy vs. fluency)
      • SMT vs. NMT
      • Perception vs. reality
      • Alternative methods of machine assistance
  • Conclusion
  • Acknowledgements
  • Notes
  • References
Published online: 28 November 2018
DOI logo
https://doi.org/10.1075/ts.18014.moo
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