Cover not available
Part of
Translation in Transition: Human and machine intelligence
Edited by Isabel Lacruz
[American Translators Association Scholarly Monograph Series XX] 2023
► pp. 39–56

Chapter 3
Post-editing and a sustainable future for translators

| Rikkyo University
Many translators and language service providers (LSPs) emphasize a distinction between human translation (HT) and post-editing (PE). As the perceived divide between the two tasks grows, the differing social status of HT and PE becomes more pronounced. This has consequences for professional identity as well as compensation. But are HT and PE substantially different? We examine this question, mainly by reinterpreting previous studies in translation process research. Based on these findings, we argue that HT and PE are not radically different in terms of the skills and effort required to achieve their shared goal of ensuring consistent, quality translations. Therefore, the perception among practitioners and LSPs of PE being easier and of lesser quality than HT may be baseless.
Keywords: human translation, post-editing, machine translation, sustainable translation
Article outline
  • Introduction
  • The status quo
    • Use of machine translation in the industry
    • MT usage by LSPs in Japan and other countries
    • ISO 18587: International standards for post-editing
    • Full post-editing
    • Light post-editing
    • Differences by translation production process
    • Post-editing and the future of the translation industry
    • The HT and PE divide
  • Previous research on PE
    • Efficiency
    • Quality
    • Effort and amount of editing
    • Pause analysis
    • The problem of inter-experimental comparison
  • Is there a difference between PE and HT?
    • Post-editing of NMT
    • Relationship between MT error and cognitive load
    • Comprehensive translation and search skills
  • Conclusion: For a sustainable future of translation
  • Notes
  • References
Published online: 26 July 2023
DOI logo
https://doi.org/10.1075/ata.xx.03yam
References (36)
References
ALPAC 1966. ALPAC Report, Language and Machines – Computers in Translation and Linguistics. A Report by the Automatic Language Processing Advisory Committee, Washington, DC.Google Scholar
Burchardt, Aljoscha., and Arle Richard Lommel. 2014. QT-LaunchPad supplement 1: Practical guidelines for the Use of MQM in Scientific Research on Translation Quality. [URL]
Carl, Michael, and Cristina Toledo Báez. 2019. “Machine translation errors and the translation process: a study across different languages.” Journal of Specialised Translation 31: 107–132.Google Scholar
Common Sense Advisory. 2018. Machine Translation for Human Innovation. Retrieved on Aug 14, 2018: [URL]
Fiederer, Rebecca, and Sharon O’Brien. 2009. “Quality and machine translation: A realistic objective?” The Journal of Specialised Translation 11: 52–74.Google Scholar
García, Ignacio. 2010. “Is machine translation ready yet?” Target 22 (1): 7–21. DOI logoGoogle Scholar
Gaspari, Federico, Hala Almaghout, and Stephen Doherty. 2015. “A survey of machine translation competences: Insights for translation technology educators and practitioners.” Perspectives Studies in Translatology 23 (3): 1–26. DOI logoGoogle Scholar
Guerberof Arenas, Ana. 2010. “Project management and machine translation.” Multilingual 21 (3): 1–4.Google Scholar
Ikaros. 2020. “What Will Happen? What will you do? Machine Translation 2020.” Tsuyaku Hon’yaku Journal [Interpreter-Translator Journal) 2020 Summer Edition, Tokyo: Ikaros Shuppan.Google Scholar
Inokuchi, Koji. 2013. “Rikkyo SFR Translation Research Project 2013. Hon’yaku ‘kakumei’ki ni　Okeru Hon’yaku yousei [Training Translators in the Translation ‘Revolution’],” Report on the Public Symposium and Future Approaches, Rikkyo University.Google Scholar
International Standard Organization (ISO). 2015. ISO 17100. 2015. Translation services – Requirements for translation services. First edition.Google Scholar
. 2017. ISO 18587 Translation services – Post-editing of machine translation output – Requirements.Google Scholar
JTF. 2017. 2017 nendo Hon’yaku Hakusho: Dai 5 kai gyokai chosa houkokusho [FY2017 Translation White Paper: the 5th Industry Survey Report]. Japan Translation Federation (JTF).Google Scholar
Kelly, Nataly. 2014. “Why so many translators hate translation technology.” Huffpost, June 19, 2014. [URL]
Koponen, Maarit. 2016. “Is machine translation post-editing worth the effort? A survey of research into post-editing and effort.” The Journal of Specialised Translation 25: 131–148.Google Scholar
Krings, Hans. P. 2001. Repairing Texts: Empirical Investigations of Machine Translation Post-Editing Processes, (Geoffrey S. Koby, trans.), Kent, Ohio: The Kent State University Press.Google Scholar
Lacruz, Isabel, and Gregory M. Shreve. 2014. “Pauses and cognitive effort in post-editing.” In Post-editing of Machine Translation: Processes and Applications, ed. by Sharon O’Brien, Laura Winther Balling, Michael Carl, Michel Simard, and Lucia Specia, 246–272. Cambridge: Cambridge Scholars.Google Scholar
Melby, Alan. K., Paul Fields, and Jason Housley. 2014. “Assessment of post-editing via structured translation specifications.” In Post-editing of Machine Translation: Processes and Applications, ed. by Sharon O’Brien, Laura Winther Balling, Michael Carl, Michel Simard, and Lucia Specia, 274–298. Cambridge: Cambridge Scholars.Google Scholar
Moorkens, Joss, and Sharon O’Brien. 2017. Assessing user interface needs of post- editors of machine translation. In Human Issues in Translation Technology, ed. by Dorothy Kenny, 110–130. Florence: Taylor and Francis.Google Scholar
O’Brien, Sharon. 2006. “Pauses as indicators of cognitive effort in post-editing machine translation output.” Across Languages and Cultures 7: 1–21. DOI logoGoogle Scholar
Onishi, Nanami. 2020. “Translator’s research skill improves translation quality: A comparative analysis of MT and HT.” Tenth IATIS Regional Workshop at Kansai University, September 3, 2020.Google Scholar
Onishi, Nanami, and Masaru Yamada. 2020. “Why translator competence in information searching matters: An empirical investigation into differences in searching behavior between professionals and novice translators.” Invitation to Interpreting and Translation Studies 22: 1–22.Google Scholar
Plitt, Mirko, and François Masselot. 2010. “A productivity test of statistical machine translation post-editing in a typical localisation context.” The Prague Bulletin of Mathematical Linguistics 93: 7–16. DOI logoGoogle Scholar
Robert, Anne-Marie. 2013. “Did you say post-editor? Some elements of a personal journey.” The Journal of Specialised Translation 19: 29–40.Google Scholar
Sakamoto, Akiko. 2019. “Why do many translators resist post-editing? A sociological analysis using Bourdieu’s concepts.” The Journal of Specialised Translation 31: 201–216.Google Scholar
Sakamoto, Akiko, and Masaru Yamada. 2019. “Industrial translation in the AI era: a frontline honest and undercover roundtable discussion,” JTF Journal 304: 8–18.Google Scholar
Snover, Matthew, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul. 2006. “A study of translation edit rate with targeted human annotation.” In Proceedings of the 7th Conference of the Association for Machine Translation in the Americas. Association for Machine Translation in the Americas, ed. by L. Gerber et al., 223–231.Google Scholar
Sostaric, Margita, Nataša Pavlović, and Filip Boltuzic. 2019. “Domain adaptation for machine translation involving a low-resource language: Google AutoML vs. from-scratch NMT systems.” Translating and the Computer 41: 113–124.Google Scholar
Tatsumi, Midori. 2009. “Correlation between automatic evaluation metric scores, Post-editing speed, and some other factors.” In Proceedings of the MT Summit XI. Association for Machine Translation in the Americas, ed. by L. Gerber et al., 332–339.Google Scholar
Temnikova, Irina. 2010. “A cognitive evaluation approach for a controlled language post-editing experiment.” In Proceedings of the 7th International Conference on Language Resources and Evaluation. European Language Resources Association (ELRA), ed. by N. Calzolari et al., 3485–3490.Google Scholar
Vermeer, Hans. J. 2000. Skopos and Commission in Translational Action. In The Translation Studies Reader, ed. by L. Venuti, 221–232. New York, NY: Routledge.Google Scholar
Way, Andy. 2020. “The effect of NMT on translators and the translation process.” Invited Talk at 5th Translation in Transition (TT5), Oct 17, 2020.Google Scholar
Yamada, Masaru. 2019. “The impact of Google Neural Machine Translation on post-editing by student translators.” The Journal of Specialised Translation 31: 87 – 106.Google Scholar
. 2020. “Post-editing and a sustainable future for translators.” Invited Talk at 5th Translation in Transition (TT5), Oct 16, 2020.Google Scholar
. 2021. “Post-edit to jizoku kanou na hon’yaku no mirai [Post-editing and a sustainable future for translators].” Bulletin: Faculty of Foreign Language Studies, Kansai University 24: 83–105.Google Scholar
Zhechev, Ventsislav. 2014. Analysing the post-editing of machine translation at Autodesk. In Post-editing of Machine Translation: Processes and Applications, ed. by Sharon O’Brien, Laura Winther Balling, Michael Carl, Michel Simard and Lucia Specia, 2–13. Cambridge: Cambridge Scholars.Google Scholar
Cited by (1)

Cited by one other publication

Lacruz, Isabel
2023. Chapter 1. Translation in transition. In Translation in Transition [American Translators Association Scholarly Monograph Series, XX], ► pp. 1 ff. DOI logo

This list is based on CrossRef data as of 4 july 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.