Volume 11, Issue 2
  • ISSN 2211-3711
  • E-ISSN: 2211-372X
Buy:$35.00 + Taxes



This article presents the results of a study involving the translation of a short story by Kurt Vonnegut from English to Catalan and Dutch using three modalities: machine-translation (MT), post-editing (PE) and translation without aid (HT). Our aim is to explore creativity, understood to involve novelty and acceptability, from a quantitative perspective. The results show that HT has the highest creativity score, followed by PE, and lastly, MT, and this is unanimous from all reviewers. A neural MT system trained on literary data does not currently have the necessary capabilities for a creative translation; it renders literal solutions to translation problems. More importantly, using MT to post-edit raw output constrains the creativity of translators, resulting in a poorer translation often not fit for publication, according to experts.


Article metrics loading...

Loading full text...

Full text loading...


  1. Aziz, Wilker, Sheila Castilho, and Lucia Specia
    2012 ‘PET: A Tool for Post-Editing and Assessing Machine Translation’. LREC, 3982–87.
    [Google Scholar]
  2. Bayer-Hohenwarter, Gerrit
    2009 ‘Translational Creativity: How to Measure the Unmeasurable’. InBehind the Mind: Methods, Models and Results in Translation Process Research, edited bySusanne Göpferich, Arnt Lykke Jakobsen, and Inger M. Mees, 371:39–59. Copenhagen: Samfundslitteratur.
    [Google Scholar]
  3. 2010 ‘Comparing Translational Creativity Scores of Students and Professionals: Flexible Problem-Solving and/or Fluent Routine Behaviour’. InNew Approaches in Translation Process Research, edited bySusanne Göpferich, Fabio Alves, and Inger M. Mees, 831:83–113. 391. Copenhagen: Samfundslitteratur.
    [Google Scholar]
  4. 2011 ‘Creative Shifts as a Means of Measuring and Promoting Translational Creativity’. Meta56 (3): 663–92. 10.7202/1008339ar
    https://doi.org/10.7202/1008339ar [Google Scholar]
  5. 2012Translatorische Kreativität: Definition – Messung – Entwicklung. Translations-Wissenschaft. Narr. https://books.google.es/books?id=z9CduAAACAAJ
    [Google Scholar]
  6. 2013 ‘Triangulating Translational Creativity Scores’. InTracks and Treks in Translation Studies: Selected Papers from the EST Congress, Leuven 2010, edited byCatherine Way, Sonia Vandepitte, Reine Meylaerts, and Magdalena Bartłomiejczyk, 1081:63–85. New York, Amsterdam: Benjamins. https://benjamins.com/catalog/btl.108. 10.1075/btl.108.04bay
    https://doi.org/10.1075/btl.108.04bay [Google Scholar]
  7. Bayer-Hohenwarter, Gerrit, and Paul Kussmaul
    2020 ‘Translation, Creativity and Cognition’. InThe Routledge Handbook of Translation and Cognition, edited byFabio Alves and Arnt Lykke Jakobsen, 1st ed., 310–25. Routledge. 10.4324/9781315178127‑21
    https://doi.org/10.4324/9781315178127-21 [Google Scholar]
  8. Busselle, Rick, and Helena Bilandzic
    2009 ‘Measuring Narrative Engagement’. Media Psychology12 (4): 321–47. 10.1080/15213260903287259
    https://doi.org/10.1080/15213260903287259 [Google Scholar]
  9. Daems, Joke
    2021 ‘Wat denken literaire vertalers echt over technologie?’ Tijdschrift over vertalen Filter, 24January 2021.
    [Google Scholar]
  10. Fontanet, Mathilde
    2005 ‘Temps de créativité en traduction’. Meta50 (2): 432–47. 10.7202/010992ar
    https://doi.org/10.7202/010992ar [Google Scholar]
  11. Fonteyne, Margot, Arda Tezcan, and Lieve Macken
    2020 ‘Literary Machine Translation under the Magnifying Glass: Assessing the Quality of an NMT-Translated Detective Novel on Document Level’. InProceedings of the 12th Language Resources and Evaluation Conference, 3790–98. Marseille, France: European Language Resources Association. https://www.aclweb.org/anthology/2020.lrec-1.468
    [Google Scholar]
  12. Guerberof-Arenas, Ana, and Antonio Toral
    2020 ‘The Impact of Post-Editing and Machine Translation on Creativity and Reading Experience’. Translation Spaces. John Benjamins. 10.1075/ts.20035.gue
    https://doi.org/10.1075/ts.20035.gue [Google Scholar]
  13. Guilford, Joy Paul
    1950 ‘Creativity’. American Psychologist, no.5: 444–54. 10.1037/h0063487
    https://doi.org/10.1037/h0063487 [Google Scholar]
  14. Hakemulder, Jemeljan F.
    2004 ‘Foregrounding and Its Effect on Readers’ Perception’. Discourse Processes38 (2): 193–218. 10.1207/s15326950dp3802_3
    https://doi.org/10.1207/s15326950dp3802_3 [Google Scholar]
  15. Heiden, Tanja
    2005 ‘Blick in die Black Box: Kreative Momente im Übersetzungsprozess: eine experimentelle Studie mit Translog’. Meta50 (2): 448–72. 10.7202/010993ar
    https://doi.org/10.7202/010993ar [Google Scholar]
  16. Kenny, Dorothy
    2006 ‘Creativity in Translation: Opening up the Corpus-Based Approach’. InProceedings of the Conference Held on 12th November 2005 in Portsmouth, 72–81. Portsmouth: University of Portsmouth.
    [Google Scholar]
  17. Kenny, Dorothy, and Marion Winters
    2020 ‘Machine Translation, Ethics and the Literary Translator’s Voice’. Translation Spaces, Fair MT: Towards ethical, sustainable Machine Translation, 9 (1): 123–49. 10.1075/ts.00024.ken
    https://doi.org/10.1075/ts.00024.ken [Google Scholar]
  18. Kolb, Waltraud
    2021 ‘Literary Translation and Post-Editing: Priming Effects and Engagement with the Text’. InComputer-Assisted Literary Translation Conference CALT2021@Swansea, 231. https://docs.google.com/document/u/1/d/e/2PACX-1vRWAAgOpMCWqGniXJAULzCra1e4We2U7lQj53e7qbKNIgHvjLGmHrwlsqbqCJCQIwJ11tztjQVmXMu6/pub
    [Google Scholar]
  19. Koponen, Maarit, Umut Sulubacak, Kaisa Vitikainen, and Jörg Tiedemann
    2020 ‘MT for Subtitling: User Evaluation of Post-Editing Productivity’. InProceedings of the 22nd Annual Conference of the European Association for Machine Translation, 115–24. Lisboa, Portugal: European Association for Machine Translation. https://www.aclweb.org/anthology/2020.eamt-1.13
    [Google Scholar]
  20. Kussmaul, Paul
    1991 ‘Creativity in the Translation Process: Empirical Approaches’. InTranslation Studies: The State of the Art. Proceedings from the First James S. Holmes Symposium on Translation Studies, edited byKitty M. van Leuven-Zwart and Ton Naaijkens, 91–101. Issue 9 of Approaches to Translation Studies. Amsterdam: Rodopi.
    [Google Scholar]
  21. 1995 ‘Creativity in Translation’. InTraining the Translator. Benjamins Translation Library, v. 10. Amsterdam: John Benjamins Publishing. 10.1075/btl.10
    https://doi.org/10.1075/btl.10 [Google Scholar]
  22. Lacruz, Isabel, Michael Denkowski, and Alon Lavie
    2014 ‘Cognitive Demand and Cognitive Effort in Post-Editing’. InProceedings of the 11th Conference of the Association For Machine Translation in the Americas, Workshop on Post-Editing Technology and Practice (WPTP-3), 73–84. Vancouver, BC, Canada.
    [Google Scholar]
  23. Lee, Katherine, Orhan Firat, Ashish Agarwal, Clara Fannjiang, and David Sussillo
    2018 ‘Hallucinations in Neural Machine Translation’, September. https://openreview.net/forum?id=SkxJ-309FQ
  24. Malmkjær, Kirsten
    2019Translation and Creativity. London and New York: Routledge: Taylor & Francis Group. 10.4324/9781315648958
    https://doi.org/10.4324/9781315648958 [Google Scholar]
  25. Mohar, Tjaša, Sara Orthaber, and Tomaž Onič
    2020 ‘Machine Translated Atwood: Utopia or Dystopia?’ ELOPE: English Language Overseas Perspectives and Enquiries17 (1): 125–41. 10.4312/elope.17.1.125‑141
    https://doi.org/10.4312/elope.17.1.125-141 [Google Scholar]
  26. 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): 240–62. 10.1075/ts.18014.moo
    https://doi.org/10.1075/ts.18014.moo [Google Scholar]
  27. Nuland, Sherwin B.
    1995How We Die: Reflections of Life’s Final Chapter, New Edition. 1st edition. New York: Vintage.
    [Google Scholar]
  28. O’Sullivan, Carol
    2013 ‘Creativity’. InHandbook of Translation Studies, edited byYves Gambier and Luc van Doorslaer, 41:42–46. Amsterdam: John Benjamins Publishing Company. 10.1075/hts.4.cre1
    https://doi.org/10.1075/hts.4.cre1 [Google Scholar]
  29. Pedersen, Jan
    2011Subtitling Norms for Television: An Exploration Focussing on Extralinguistic Cultural References. John Benjamins Publishing. 10.1075/btl.98
    https://doi.org/10.1075/btl.98 [Google Scholar]
  30. Rojo, Ana
    2017 ‘The Role of Creativity’. InThe Handbook of Translation and Cognition, edited byJohn W. Schwieter and Aline Ferreira, 1st ed., 350–68. Wiley. 10.1002/9781119241485.ch19
    https://doi.org/10.1002/9781119241485.ch19 [Google Scholar]
  31. Rojo, Ana, and María Ramos Caro
    2016 ‘Can Emotion Stir Translation Skill? Defining the Impact of Positive and Negative Emotions on Translation Performance’. InReembedding Translation Process Research, edited byRicardo Muñoz, 1281:107–30. Benjamins Translation Library. Amsterdam: John Benjamins Publishing. 10.1075/btl.128.06roj
    https://doi.org/10.1075/btl.128.06roj [Google Scholar]
  32. Rudowicz, Elisabeth
    2003 ‘Creativity and Culture: A Two Way Interaction’. Scandinavian Journal of Educational Research47 (3): 273–90. 10.1080/00313830308602
    https://doi.org/10.1080/00313830308602 [Google Scholar]
  33. Ruffo, Paola
    2021 ‘In-between Role and Technology: Literary Translators on Navigating the New Socio-Technological Paradigm’. Edinburgh: Heriot-Watt University.
    [Google Scholar]
  34. Runco, Mark A.
    2014Creativity. Second edition. Academic Press.
    [Google Scholar]
  35. Runco, Mark A., and Jarret J. Jaeger
    2012 ‘The Standard Definition of Creativity’. Creativity Research Journal24 (1): 92–96. 10.1080/10400419.2012.650092
    https://doi.org/10.1080/10400419.2012.650092 [Google Scholar]
  36. Şahin, Mehmet, and Sabri Gürses
    2019 ‘Would MT Kill Creativity in Literary Retranslation?’ InProceedings of the Qualities of Literary Machine Translation, 26–34. Dublin, Ireland: European Association for Machine Translation. https://www.aclweb.org/anthology/W19-7304
    [Google Scholar]
  37. Sautoy, Marcus Du
    2019The Creativity Code: How AI Is Learning to Write, Paint and Think. 4th Estate. 10.2307/j.ctv2sp3dpd
    https://doi.org/10.2307/j.ctv2sp3dpd [Google Scholar]
  38. Sawyer, R. Keith
    2006Explaining Creativity: The Science of Human Innovation. Oxford ; New York: Oxford University Press.
    [Google Scholar]
  39. Schmidtke, Dag, and Declan Groves
    2019 ‘Automatic Translation for Software with Safe Velocity’. InProceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks, 159–66. Dublin: European Association for Machine Translation. https://www.aclweb.org/anthology/W19-6729
    [Google Scholar]
  40. Slessor, Stephen
    2020 ‘Tenacious Technophobes or Nascent Technophiles? A Survey of the Technological Practices and Needs of Literary Translators’. Perspectives28 (2): 238–52. 10.1080/0907676X.2019.1645189
    https://doi.org/10.1080/0907676X.2019.1645189 [Google Scholar]
  41. Snover, Matthew, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul
    2006 ‘A Study of Translation Edit Rate with Targeted Human Annotation’. Proceedings of Association for Machine Translation in the Americas2001: 223–31.
    [Google Scholar]
  42. Tezcan, Arda, Joke Daems, and Lieve Macken
    2019 ‘When a “Sport” Is a Person and Other Issues for NMT of Novels’. In, 111.
    [Google Scholar]
  43. Toral, Antonio, Andreas van Cranenburgh, and Tia Nutters
    2021 ‘Literary-Adapted Machine Translation in a Well-Resourced Language Pair’. InProceedings of the 7th Conference of The International Association for Translation and Inter- Cultural Studies (IATIS). Barcelona.
    [Google Scholar]
  44. Toral, Antonio, Antoni Oliver, and Pau Ribas-Bellestín
    2020 ‘Machine Translation of Novels in the Age of Transformer’. InMaschinelle Übersetzung für Übersetzungsprofis, edited byJörg Porsiel, 276–96. Berlin: BDÜ Fachverlag.
    [Google Scholar]
  45. Toral, Antonio, Martijn Wieling, and Andy Way
    2018 ‘Post-Editing Effort of a Novel With Statistical and Neural Machine Translation’. Frontiers in Digital Humanities51: 1–11. 10.3389/fdigh.2018.00009
    https://doi.org/10.3389/fdigh.2018.00009 [Google Scholar]
  46. Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin
    2017 ‘Attention Is All You Need’. InAdvances in Neural Information Processing Systems 30, edited byI. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, 5998–6008. Curran Associates, Inc.papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
    [Google Scholar]
  47. Vieira, Lucas, Xiaochun Zhang, Roy Yodaule, and Michael Carl
    2020 ‘Machine Translation and Literary Texts: A Network of Possibilities’. Presented at theCreative Translation and Technologies Expert Meeting, University of Surrey, May 29. https://surrey-ac.zoom.us/rec/play/uJcodr2u-zM3HtSRsgSDCv5xW420eqOs1CQb_KFfxEm8B3IDNValMLYTM7FO1h7mhKk4iDA5fD0Ac0Ah
    [Google Scholar]
  48. Vonnegut, Kurt
    1999Bagombo Snuff Box. United States: G. P. Putnam’s Sons.
    [Google Scholar]
  49. Webster, Rebecca, Margot Fonteyne, Arda Tezcan, Lieve Macken, and Joke Daems
    2020 ‘Gutenberg Goes Neural: Comparing Features of Dutch Human Translations with Raw Neural Machine Translation Outputs in a Corpus of English Literary Classics’. Informatics7 (3): 32. 10.3390/informatics7030032
    https://doi.org/10.3390/informatics7030032 [Google Scholar]

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error