Volume 13, Issue 4
  • ISSN 1878-9714
  • E-ISSN: 1878-9722
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Neural machine translation (NMT), proven to be productively and qualitatively competitive, creates great challenges and opportunities for stakeholders in both the market and the education contexts. This paper explores how English-Chinese NMT post-editing (PE) is accepted in China from the perspectives of attitude, practice, and training, based on an integrative digital survey with role-specific popup questions for translators and clients in the market setting, and for translation teachers and students in the education setting. Descriptive statistics and correlation analyses of the survey data suggest Chinese stakeholders’ generally moderate view of PE, with outsiders like clients being more optimistic about PE than are insiders like translators. In the market setting, most translators use PE to different degrees in translating primarily informative texts; here, affiliated translators report a more frequent usage, and employ more sophisticated tools than do part-time or freelance translators. Whereas translators, on the whole, fail to notify clients of their own PE usage, or to charge clients for PE and human translation (HT) differently, most clients express their willingness to accept high-quality PE output for the sake of saving cost and time. In the education setting, despite students’ concealed usage of PE to do HT assignments to varying degrees, and their wish to learn PE out of concern for their future career, PE is generally not taught in translation classrooms of Chinese universities in the form of teaching PE as a course or integrating PE content into traditional translation course.


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  1. Báez, M. C. Toledo
    2018 “Machine translation and post-editing: Impact of training and directionality on quality and productivity.” Revista Tradumàtica (16): 24–34.
    [Google Scholar]
  2. Daems, Joke, and Lieve Macken
    2020 “Post-editing human translations and revising machine translations: impact on efficiency and quality.” InTranslation Revision and/or Post-editing: Industry Practices and Cognitive Processes, ed. byMaarit Koponen, Brian Mossop, Isabelle S. Robert, and Giovanna Scocchera, 50–70. London: Routledge. 10.4324/9781003096962‑5
    https://doi.org/10.4324/9781003096962-5 [Google Scholar]
  3. Daems, Joke, Sonia Vandepitte, Robert J. Hartsuiker, and Lieve Macken
    2017 “Identifying the machine translation error types with the greatest impact on post-editing effort”. Frontiers in Psychology8(1282): 1–15. 10.3389/fpsyg.2017.01282
    https://doi.org/10.3389/fpsyg.2017.01282 [Google Scholar]
  4. Drugan, Jo, and Bogdan Babych
    2010 “Shared resources, shared values? Ethical implications of sharing translation resources.” InProceedings of the Second Joint EM +/CNGL Workshop: Bringing MT to the User, ed. byVentsislav Zhechev, 3–9. Denver, Colo.: Association for Machine Translation in the Americas.
    [Google Scholar]
  5. Feng, Quangong, and Huiyu Zhang
    2015 “To train post-editors in the background of the global language services industry.” Foreign Language World166 (1): 65–72.
    [Google Scholar]
  6. Feng, Quangong, and Jiawei Li
    2016 “Investigation on post-editing of machine-translated news.” Technology Enhanced Foreign Language Education172(6): 74–79.
    [Google Scholar]
  7. Ginovart Cid, Clar, and Carmen Colominas Ventura
    2020 “The MT post-editing skill set: course descriptions and educators’ thoughts.” InTranslation Revision and Post-editing: Industry Practices and Cognitive Processes, ed. byMaarit Koponen, Brian Mossop, Isabelle S. Robert, and Giovanna Scocchera, 226–246. London: Routledge.
    [Google Scholar]
  8. ISO
    ISO 2017 Translation Services: Post-editing of Machine Translation Output-Requirements (ISO/DIS 18587). https://www.iso.org/standard/62970.html (consulted8 April 2019).
  9. Jia, Yanfang, Michael Carl, and Xiangling Wang
    2019 “How does the post-editing of neural machine translation compare with from-scratch translation? A product and process study.” The Journal of Specialised Translation311: 60–86.
    [Google Scholar]
  10. Kenny, Dorothy, and Stephen Doherty
    2014 “Statistical machine translation in the translation curriculum: Overcoming obstacles and empowering translators.” The Interpreter and Translator Trainer8(2): 276–294. 10.1080/1750399X.2014.936112
    https://doi.org/10.1080/1750399X.2014.936112 [Google Scholar]
  11. Koponen, Maarit, and Leena Salmi
    2017 “Post-editing quality: Analysing the correctness and necessity of post-editor corrections.” Linguistica Antverpiensia16 (1):137–148.
    [Google Scholar]
  12. Läubli, Samuel, Chantal Amrhein, Patrick Düggelin, Beatriz Gonzalez, Alena Zwahlen, and Martin Volk
    2019 “Post-editing productivity with neural machine translation: An empirical assessment of speed and quality in the banking and finance domain.” InProceedings of MT Summit XVII (Volume 1): Research Track, ed. byMikel Forcada, Andy Way, Barry Haddow, and Rico Sennrich, 267–272. Dublin: European Association for Machine Translation.
    [Google Scholar]
  13. Lu, Zhi, and Juan Sun
    2018 “An eye-tracking study of cognitive processing in human translation and post-editing.” Foreign Language Teaching and Research50(5): 760–769.
    [Google Scholar]
  14. Moorkens, Joss, and Sharon O’Brien
    2015 “Post-Editing Evaluations: Trade-offs between Novice and Professional Participants.” InProceedings of the 18th Annual Conference of the European Association for Machine Translation, ed. byİlknur Durgar El-Kahlout, Mehmed Özkan, Felipe Sánchez-Martínez, Gema Ramírez-Sánchez, Fred Hollowood and Andy Way, 75–81. Stroudsburg, Penn.: Association for Computational Linguistics.
    [Google Scholar]
  15. Moorkens, Joss, Antonio Toral, Sheila Castilho, and Andy Way
    2018 “Translators’ perceptions of literary post-editing using statistical and neural machine translation.” Translation Spaces7(2):240–262. 10.1075/ts.18014.moo
    https://doi.org/10.1075/ts.18014.moo [Google Scholar]
  16. O’Brien, Sharon, Laura W. Balling, Michael Carl, Michel Simard and Lucia Specia
    2014Post-editing of Machine Translation: Processes and Applications. Newcastle: Cambridge Scholars Publishing.
    [Google Scholar]
  17. Ortiz-Boix, Carla, and Anna Matamala
    2017 “Assessing the quality of post-edited wildlife documentaries.” Perspectives: Studies in Translation Theory and Practice25(4):571–593. 10.1080/0907676X.2016.1245763
    https://doi.org/10.1080/0907676X.2016.1245763 [Google Scholar]
  18. Pilos, Spyridon
    2011 “Machine Translation at the European Commission.” InMeta-Forum 2011-Solutions for Multilingual Europe. Budapest: Hungarian Presidency of the Council of the European Union.
    [Google Scholar]
  19. Rossi, Caroline
    2017 “Introducing statistical machine translation in translator training: from uses and perceptions to course design, and back again.” Revista Tradumàtica151: 48–62. 10.5565/rev/tradumatica.195
    https://doi.org/10.5565/rev/tradumatica.195 [Google Scholar]
  20. Rossi, Caroline, and Jean-Pierre Chevrot
    2019 “Uses and perceptions of Machine Translation at the European Commission.” The Journal of Specialised Translation (31): 177–200.
    [Google Scholar]
  21. Screen, Benjamin
    2017 “Productivity and quality when editing machine translation and translation memory outputs: an empirical analysis of English to Welsh translation.” Studia Celtica Posnaniensia2(1):1–24. 10.1515/scp‑2017‑0007
    https://doi.org/10.1515/scp-2017-0007 [Google Scholar]
  22. Shao, Nan
    2020Analysis of Machine Translation Errors and Corresponding Pre-editing and Post-editing Strategies. Beijing: Beijing International Studies University.
    [Google Scholar]
  23. Sismat, Muhamad A. B. H.
    2016 “Quality and productivity: A comparative analysis of human translation and post-editing with Malay learners of Arabic and English”. PhD thesis, University of Leeds.
    [Google Scholar]
  24. Sosoni, Vilelmini, Maria Stasimioti, and Katia-Lida Kermanidis
    2018 “On post-editing effort: Testing translator perceptions.” InTranslating Europe Workshop “The Changing Profile of the Translator Profession: Technology, Competences and Fit-for-market Training”, 23–24. Corfu: Ionian University.
    [Google Scholar]
  25. Sun, Dongyun
    2017 “Application of Post-Editing in Foreign Language Teaching: Problems and Challenges.” Canadian Social Science13(7): 1–5.
    [Google Scholar]
  26. TAUS
    TAUS 2013 Pricing machine translation post-editing guidelines. https://www.taus.net/academy/best-practices/postedit-best-practices/pricing-machine-translation-post-editing-guidelines (consulted8 April 2019).
  27. TAUS
    TAUS 2015 Machine translation post-editing guidelines. https://www.taus.net/academy/best-practices/postedit-best-practices/machine-translation-post-editing-guidelines (consulted8 April 2019).
  28. Translators Association of China
    Translators Association of China 2021 China Language Service Industry Development Report. Translators Association of China.
    [Google Scholar]
  29. Venkatesh, Viswanath and Hillol Bala
    2008 “Technology acceptance model 3 and a research agenda on interventions.” Decision Sciences39(2): 273–315. 10.1111/j.1540‑5915.2008.00192.x
    https://doi.org/10.1111/j.1540-5915.2008.00192.x [Google Scholar]
  30. Wang, Xiangling, and Tingting Wang
    2019 “A comparative study of human translation and machine translation post-editing in E-C Translation: Translation speed, quality and translators’ attitude.” Foreign Languages and Cultures3(4): 83–93.
    [Google Scholar]
  31. Yamada, Masaru
    2015 “Can college students be post-editors? An investigation into employing language learners in machine translation plus post-editing settings.” Machine Translation29(1): 49–67. 10.1007/s10590‑014‑9167‑7
    https://doi.org/10.1007/s10590-014-9167-7 [Google Scholar]
  32. Zhao, Shengfang
    2021 “Post-editing neural machine translation versus human translation for Chinese essays: A pilot study.” InDiverse Voices in Chinese Translation and Interpreting: Theory and Practice, ed. byRiccardo Moratto and Martin Woesler, 399–432. Singapore: Springer. 10.1007/978‑981‑33‑4283‑5_17
    https://doi.org/10.1007/978-981-33-4283-5_17 [Google Scholar]

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