733
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Machine translation and culture-bound texts in translator education: a pilot study

ORCID Icon & ORCID Icon
Pages 503-525 | Received 02 Sep 2022, Accepted 16 Jul 2023, Published online: 08 Aug 2023

References

  • Allott, N. 2010. Key Terms in Pragmatics. London: Continuum.
  • Bartmiński, J. 2009. Aspects of Cognitive Ethnolinguistics, Translated by Adam Głaz, Jörg Zinken. London: Equinox.
  • Carl, M., and C. T. Báez. 2019. “Machine Translation Errors and the Translation Process: A Study Across Different Languages.” Journal of Specialised Translation 31:107–132.
  • Ciobanu, D. 2022. “Rule-Based, Statistical and Neural Machine Translation: What Now?“ In The Many Faces of Translation. Machine Translation: Driven by Humans, Powered by Technology, DG TRAD Conference 2021, 8–37. Brussels: European Parliament.
  • Daems, J. 2022. “Dutch Literary Translators’ Use and Perceived Usefulness of Technology. The Role of Awareness and Attitude.” In Using Technologies for Creative-Text Translation, edited by J. L. Hadley, K. Taivalkoski-Shilov, C. Teixeira, and A. Toral, 40–65. New York and London: Routledge. https://doi.org/10.4324/9781003094159-3.
  • Daems, J., and L. Macken. 2020. “Post-Editing Human Translations and Revising Machine Translations: Impact on Efficiency and Quality.” In Translation Revision And/Or Post-Editing: Industry Practices and Cognitive Processes, edited by M. Koponen, B. Mossop, I. Robert, and G. Scocchera, 50–70. London-and New York: Routledge. https://doi.org/10.4324/9781003096962-5.
  • Daems, J., S. Vandepitte, R. Hartsuiker, and L. Macken. 2015. “The Impact of Machine Translation Error Types on Post-Editing Effort Indicators.” In Fourth Workshop on Post-Editing Technology and Practice: Proceedings, 31–45. https://biblio.ugent.be/publication/6990271/file/6990287.pdf.
  • Daems, J., S. Vandepitte, R. Hartsuiker, and L. Macken. 2017. “Translation Methods and Experience: A Comparative Analysis of Human Translation and Post-Editing with Students and Professional Translators.” Meta 62 (2): 245–270. https://doi.org/10.7202/1041023ar.
  • Enfield, N. J. 2004. “Ethnosyntax: Introduction.” In Ethnosyntax: Explorations in Grammar and Culture, edited by N. J. Enfield, 5–30. Oxford: Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199266500.003.0001.
  • Goodfellow, I., Y. Bengio, and A. Courville. 2018. L’apprentissage profond. Translated by AI and postedited by F. Navarro, S. El Kolei, B. Guedj, and C. Chesneau. Massot Éditions.
  • Guerberof Arenas, A., and J. Moorkens. 2019. “Machine Translation and Post-Editing Training as Part of a Master’s Programme.” Journal of Specialised Translation 31: 217–238.
  • Guerberof Arenas, A., and A. Toral. 2022. “Creativity in Translation. Machine Translation as a Constraint for Literary Texts.” Translation Spaces 11 (2): 184–212. https://doi.org/10.1075/ts.21025.gue.
  • Hao, Y., and A. Pym. 2021. “Translation Skills Required by Master’s Graduates for Employment: Which are Needed, Which are Not?” Across Languages and Cultures 22 (2): 158–175. https://doi.org/10.1556/084.2021.00012.
  • Hervey, S., and I. Higgins. [1992] 2002. Thinking French Translation. A Course in Translation Method: French to English. 2nd ed. London: Routledge. https://doi.org/10.4324/9780203167120.
  • Hitchcock, D. 2022. “Critical Thinking.” In The Stanford Encyclopedia of Philosophy (Winter 2022 Edition), edited by E. N. Zalta and U. Nodelman, https://plato.stanford.edu/archives/win2022/entries/critical-thinking.
  • Hvelplund, K. T. 2022. “Institutional Translation and the Translation Process. Cognitive Resources, Digital Resources, and Translator Training.” In Institutional Translator Training, edited by T. Svoboda, Ł. Biel, and V. Sosoni, 92–110. London and New York: Routledge. https://doi.org/10.4324/9781003225249-7.
  • ISO (International Organization for Standardization). 2015. ISO 17100:2015 Translation Services – Requirements for Translation Services.
  • ISO (International Organization for Standardization). 2017. ISO 18587:2017 Translation Services — Post-Editing of Machine Translation Output — Requirements.
  • ISO (International Organization for Standardization). 2022. ISO/DIS 5060:2022 (En), Translation Services — Evaluation of Translation Output — General Guidance. https://www.iso.org/obp/ui/#iso:std:iso:5060:dis:ed-1:v1:en.
  • Katan, D. [1999] 2004. Translating Cultures: An Introduction for Translators, Interpreters and Mediators. 2nd ed. Manchester: St. Jerome.
  • Kenny, D., edited by 2022. Machine Translation for Everyone: Empowering Users in the Age of Artificial Intelligence. Berlin: Language Science Press.
  • Kornacki, M., and P. Pietrzak. 2021. “New Translator Training Environments: Towards Improving Translation Students’ Digital Resilience.” New Voices in Translation Studies 24:1–22.
  • Krings, H. P. 2001. Repairing Texts: Empirical Investigations of Machine Translation Postediting Processes. Kent: The Kent State University Press.
  • Łoboda, K. 2021. “Beyond MT Metrics in Specialised Translation: Automated and Manual Evaluation of Machine Translation Output for Freelance Translators and Small LSPs in the Context of EU Documents.” Beyond Philology an International Journal of Linguistics, Literary Studies and English Language Teaching 17 (4): 45–73. https://doi.org/10.26881/bp.2020.4.02.
  • Lommel, A., M. Popović, and A. Burchardt. 2014. “Assessing Inter-Annotator Agreement for Translation Error Annotation.” In Automatic and Manual Metrics for Operational Translation Evaluation: Workshop Proceedings, edited by K. J. Miller, L. Specia, K. Harris, and S. Bailey, 31–37. mte2014.github.io/MTE2014-Workshop-Proceedings.pdf.
  • Marczak, M. 2018. “Translation Pedagogy in the Digital Age.” Angles 7 (7): 1–19. http://journals.openedition.org/angles/895.
  • Mastela, O. 2020a. “Retelling Legends and Folk Tales: A Transcreative Approach in the Collaborative Translation Classroom.” Research in Language 18 (2): 151–171. https://doi.org/10.18778/1731-7533.18.2.03.
  • Mastela, O., ed. 2020b. Retellings of Selected Polish Legends in English: Exercises in Literary Transcreation. Vol. 4. Kraków: Katedra Przekładoznawstwa UJ.
  • Mastela, O. 2022. “Zaangażowana dydaktyka akademicka i jej wpływ na jakość kształcenia tłumaczy [Engaged didactics and its impact on the quality of translator education].” In Jakość kształcenia akademickiego [The Quality of Academic Teaching], edited by J. M. Bugaj and M. Budzanowska-Drzewiecka, 181–198. Kraków: Wydawnictwo Uniwersytetu Jagiellońskiego.
  • Mastela, O. 2023. “A Holistic, Interdisciplinary and Socially Engaged Approach to Translator Education: Learning Through Meaningful Projects.” Między Oryginałem a Przekładem 4 ( forthcoming).
  • Moorkens, J., S. Castilho, F. Gaspari, and S. Doherty, eds. 2018. Translation Quality Assessment: From Principles to Practice. Berlin: Springer International Publishing. https://doi.org/10.1007/978-3-319-91241-7.
  • Mossop, B. 2001. Revising and Editing for Translators. Manchester: St. Jerome.
  • MQM Core Typology. [Update April 10th] 2023. www.themqm.info/typology/.
  • Nitzke, J., and S. Hansen-Schirra. 2021. A Short Guide to Post-Editing. Berlin: Language Science Press.
  • O’Brien, S. 2002. “Teaching Post-Editing: A Proposal for Course Content.” In Proceedings of the 6th EAMT Workshop: Teaching Machine Translation, 99–106. https://aclanthology.org/2002.eamt-1.
  • O’Brien, S. 2007. “An Empirical Investigation of Temporal and Technical Post-Editing Effort.” Translation & Interpreting Studies 2 (1): 83–136. https://doi.org/10.1075/tis.2.1.03ob.
  • Oppman, A. 1925. Legendy warszawskie. Poznań: Księgarnia św. Wojciecha.
  • Ozga, K. [2008] 2017. “Chłop i czarnoksiężnik.” In W cieniu Palany Gery: Legendy z okolic Dębicy, edited by K. Ozga, 83–85. 3rd ed. Dębica: Stowarzyszenie Słowian Doliny Wisłoki.
  • Papineni, K., S. Roukos, T. Ward, and W.-J. Zhu. 2002. “BLEU: A Method for Automatic Evaluation of Machine Translation”. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, Pennsylvania, USA. Association for Computational Linguistics, 311–318.
  • Piotrowska, M. 2002. A Compensational Model for Strategy and Techniques in Teaching Translation. Kraków: Wydawnictwo Naukowe Akademii Pedagogicznej.
  • Piotrowska, M. 2022. Translation. Inspirations We Live by. Kraków: Księgarnia Akademicka. https://doi.org/10.12797/9788381388078.
  • Popel, M., M. Tomkova, J. Tomek, Ł. Kaiser, J. Uszkoreit, O. Bojar, and Z. Žabokrtský. 2020. “Transforming Machine Translation: A Deep Learning System Reaches News Translation Quality Comparable to Human Professionals.” Nature Communications 11 (1): 4381. https://doi.org/10.1038/s41467-020-18073-9.
  • Rak, M. 2015. Kulturemy podhalańskie. Kraków: Księgarnia Akademicka. https://doi.org/10.12797/9788376386027.
  • Robert, I. S., I. Schrijver, and J. J. Ureel. 2022a. “Measuring Translation Revision Competence and Post-Editing Competence in Translation Trainees: Methodological Issues.” Perspectives 1–15. https://doi.org/10.1080/0907676X.2022.2030377.
  • Robert, I. S., I. Schrijver, and J. J. Ureel. 2022b. “Translation, Translation Revision and Post-Editing Competence Models: Where are We Now?” In The Human Translator in the 2020s, edited by G. Massey, E. Huertas-Barros, and D. Katan, 44–59. Routledge. https://doi.org/10.4324/9781003223344-4.
  • Robert, I. S., I. Schrijver, and J. J. Ureel. 2023. “Comparing L2 Translation, Translation Revision, and Post-Editing Competences in Translation Trainees. An Exploratory Study into Dutch–French Translation.” Babel 69 (1): 99–128. https://doi.org/10.1075/babel.00307.rob.
  • Tabakowska, E. 2002. “Bariery kulturowe są zbudowane z gramatyki.” In Przekład – Język – Kultura, edited by R. Lewicki, 25–34. Lublin: Wydawnictwo UMCS.
  • Tamchyna, A. 2020. “Selection of MT Systems in Translation Workflows.” Proceedings of the 14th Conference of the Association for Machine Translation in the Americas 2: 270–291.
  • Toral, A., and A. Way. 2018. “What Level of Quality Can Neural Machine Translation Attain on Literary Text?” In Translation Quality Assessment, edited by J. Moorkens, S. Castilho, F. Gaspari, and S. Doherty, 263–287. Springer International Publishing. https://doi.org/10.1007/978-3-319-91241-7_12.
  • Toral, A., M. Wieling, and A. Way. 2018. “Post-Editing Effort of a Novel with Statistical and Neural Machine Translation.” Frontiers in Digital Humanities 5:9. https://doi.org/10.3389/fdigh.2018.00009.
  • Vanmassenhove, E., D. Shterionov, and A. Way. 2019. “Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation.” Proceedings of MT Summit XVII (1): 222–232.
  • Webster, R., M. Fonteyne, A. Tezcan, L. Macken, and J. Daems. 2020. “Gutenberg Goes Neural: Comparing Features of Dutch Human Translations with Raw Neural Machine Translation Outputs in a Corpus of English Literary Classics.” Informatics 7 (3): 32–52. https://doi.org/10.3390/informatics7030032.
  • Wierzbicka, A. 1997. Understanding Cultures Through Their Key Words. New York: Oxford University Press.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.