Integrating post-editing into the subtitling classroom: what do subtitlers-to-be think?
DOI:
https://doi.org/10.52034/lans-tts.v22i.777Keywords:
Audiovisual translation, subtitling, machine translation, post-editing, translator trainingAbstract
In today’s professional landscapes, new technologies have altered media localization workflows as much as practitioners’ workstations and habits. A more comprehensive integration of automation tools, including (neural) machine translation systems, has been ushered in by the proliferation of cloud ecosystems. In a further technological drive in the production of subtitle projects, systems now integrate automatic speech recognition and can machine translate subtitles from pre-spotted templates. The rise of post-editors in media localization, specifically in subtitling, has been a reality for some time now, triggering the need for up-to-date training methods and academic curricula. It is against this backdrop that this article seeks to examine the perception of post-editing among trainees in subtitling. A total of four teaching experiences, conceived as practical experiments in interlingual subtitle post-editing (English into Spanish), involving postgraduate students from both Spain and the United Kingdom, are described here. The sample comprised 36 master’s-level students enrolled in translator training programmes that have a focus on audiovisual translation. A mixed-methods approach was adopted for this study; after each experience, the feedback collated through online questionnaires has proved paramount to understanding the participants’ opinions about post-editing in the subtitling classroom. Interestingly, most of the respondents believe that subtitle post-editing training should feature more prominently in translation curricula even though they have voiced their reluctance to undertake post-editing work professionally.
References
Allen, J. (2003). Post-editing. In H. Somers (Ed.), Computers and translation: A translator’s guide (pp. 297–317). John Benjamins. https://doi.org/10.1075/btl.35.19all
Athanasiadi, R. (2017). The potential of machine translation and other language assistive tools in subtitling: A new era? In M. Deckert (Ed.), Audiovisual translation: Research and use (pp. 29–49). Peter Lang.
ATRAE. (2021). Comunicado sobre la posedición. Asociación de Traducción y Adaptación Audiovisual de España. https://atrae.org/comunicado-sobre-la-posedicion
Bogucki, ?., & Deckert, M. (Eds.) (2020). The Palgrave handbook of audiovisual translation and media accessibility. Palgrave Macmillan. https://doi.org/10.1007/978-3-030-42105-2
Bolaños García-Escribano, A., & Declercq, C. (2023). Editing in audiovisual translation (subtitling). In S.-W. Chan (Ed.), The Routledge encyclopedia of translation technology (pp. 565–581). Routledge. https://doi.org/10.4324/9781003168348-36
Bolaños García-Escribano, A., & Díaz-Cintas, J. (2020). The cloud turn in audiovisual translation. In ?. Bogucki & M. Deckert (Eds.), The Palgrave handbook of audiovisual translation and media accessibility (pp. 519–544). Palgrave Macmillan. https://doi.org/10.1007/978-3-030-42105-2_26
Bolaños García-Escribano, A., Díaz-Cintas, J., & Massidda, S. (2021). Subtitlers on the cloud: The use of professional web-based systems in subtitling practice and training. Tradumàtica, 19, 1–21. https://doi.org/10.5565/rev/tradumatica.276
British Standards Institute (BSI). (2015). Translation services – Post-editing of machine translation – Requirements.
Burchardt, A., Lommel, A., Bywood, L., Harris, K., & Popovi?, M. (2016). Machine translation quality in an audiovisual context. Target, 28(2), 206–221. https://doi.org/10.1075/target.28.2.03bur
Bywood, L., Georgakopoulou, P., & Etchegoyhen, T. (2017). Embracing the threat: Machine translation as a solution for subtitling. Perspectives, 25(3), 492–508. https://doi.org/10.1080/0907676X.2017.1291695
Columbus, C. (Director). (1993). Mrs Doubtfire [Film]. Twentieth Century Fox; Blue Wolf Productions.
de Sousa, S., Aziz, W., & Specia, L. (2011). Assessing the post-editing effort for automatic and semi-automatic translations of DVD subtitles. Proceedings of Recent Advances in Natural Language Processing (pp. 97–103). Association for Computational Linguistics. https://aclanthology.org/R11-1014
Declercq, C. (2023). Editing in translation technology. In S.-W. Chan (Ed.), The Routledge encyclopedia of translation technology (pp. 551–564). Routledge. https://doi.org/10.4324/9781003168348-35
Delabastita, D. (1989). Translation and mass-communication: Film and TV. Translation as evidence of cultural dynamics. Babel, 35(4), 193–218. https://doi.org/10.1075/babel.35.4.02del
Díaz-Cintas, J. (2023). Technological strides in subtitling. In S.-W. Chan (Ed.), The Routledge encyclopedia of translation technology (pp. 720–730). Routledge. https://doi.org/10.4324/9781003168348-46
Díaz-Cintas, J., & Massidda, S. (2019). Technological advances in audiovisual translation. In M. O’Hagan (Ed.), The Routledge handbook of translation and technology (pp. 255–270). Routledge. https://doi.org/10.4324/9781315311258-15
Díaz-Cintas, J., & Remael, A. (2021). Subtitling: Concepts and practices. Routledge. https://doi.org/10.4324/9781315674278
do Carmo, F., Shterionov, D., Moorkens, J., Wagner, J., Hossari, M., Paquin, E., Schmidtke, D., Groves, D., & Way, A. (2021). A review of the state-of-the-art in automatic post-editing. Machine Translation, 35(2), 101–143. https://doi.org/10.1007/s10590-020-09252-y
Easton, J. (2021, April 26). OTT revenues to top US$200 billion by 2026. Digital TV Europe. www.digitaltveurope.com/2021/04/26/ott-revenues-to-top-us200-billion-by-2026/#close-modal
Federico, M., Cattelan, A., & Trombetti, M. (2012). Measuring user productivity in machine translation enhanced computer assisted translation. Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research papers (pp. 1–10). https://aclanthology.org/2012.amta-papers.22
Gambier, Y., & Gottlieb, H. (Eds.) (2001). (Multi) media translation: Concepts, practices and research. John Benjamins. https://doi.org/10.1075/btl.34
Gaspari, F., Toral, A., Kumar Naskar, S., Groves, D., & Way, A. (2014). Perception vs reality: Measuring machine translation post-editing productivity. In S. O’Brien, M. Simard & L. Specia (Eds.), Proceedings of the 11th Conference of the Association for Machine Translation in the Americas (pp. 60–72). Association for Machine Translation in the Americas. https://aclanthology.org/2014.amta-wptp.5
Georgakopoulou, P., & Bywood, L. (2014). MT in subtitling and the rising profile of the post-editor. Multilingual, 25(1), 24–28. https://multilingual.com/articles/mt-in-subtitling-and-the-rising-profile-of-the-post-editor
González Pastor, D. (2021). Introducing machine translation in the translation classroom: A survey on students’ attitudes and perceptions. Tradumàtica, 19, 47–65. https://doi.org/10.5565/rev/tradumatica.273
Guerberof Arenas, A. (2008). Productivity and quality in the post-editing of outputs from translation memories and machine translation [Doctoral dissertation, Universitat Rovira i Virgili]. Tesis Doctorals en Xarxa. http://hdl.handle.net/10803/90247
Gupta, P. Sharma, M., Pitale, K., & Kumar, K. (2019). Problems with automating translation of movie/TV show subtitles. ArXiv. https://doi.org/10.48550/arXiv.1909.05362
Hu, K., & Cadwell, P. (2016). A comparative study of post-editing guidelines. Baltic J. Modern Computing, 4(2), 346–353. https://aclanthology.org/W16-3420
Koponen, M. (2012). Comparing human perceptions of post-editing effort with post-editing operations. In C. Callison Burch, P. Koehn, C. Monz, M. Post, R. Soricut & L. Specia (Eds.), Proceedings of the Seventh Workshop on Statistical Machine Translation (pp. 181–190). Association for Computational Linguistics. https://aclanthology.org/W12-3123
Koponen, M. (2016). Machine translation post-editing and effort: Empirical studies on the post-editing process [Doctoral dissertation, University of Helsinki]. University of Helsinki open repository. http://hdl.handle.net/10138/160256
Koponen, M., Sulubacak, U., Vitikainen, K., & Tiedemann, J. (2020a). MT for subtitling: User evaluation of post-editing productivity. Proceedings of the 22nd Annual Conference of the European Association for Machine Translation (pp. 115–124). European Association for Machine Translation. https://aclanthology.org/2020.eamt-1.13
Koponen, M., Sulubacak, U., Vitikainen, K., & Tiedemann, J. (2020b). MT for subtitling: Investigating professional translators’ user experience and feedback. Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (pp. 79–92). Association for Machine Translation in the Americas. https://aclanthology.org/2020.amta-pemdt.6
Krings, H. P. (2001). Repairing texts: Empirical investigations of machine translation post-editing processes. Kent State University Press.
Läubli, S. & Orrego-Carmona, D. (2017). When Google Translate is better than some human colleagues, those people are no longer colleagues. Translating and the Computer, 39, 59–69. https://doi.org/10.5167/uzh-147260
Matusov, E., Wilken, P., & Georgakopoulou, P. (2019). Customizing neural machine translation for subtitling. Proceedings of the Fourth Conference on Machine Translation (pp. 82–93). Association for Computational Linguistics. http://doi.org/10.18653/v1/W19-5209
Moorkens, J., Toral, A., Castilho, S., & Way, A. (2018). Translators’ perceptions of literary post-editing using statistical and neural machine translation. Translation Spaces, 7(2), 240–262. https://doi.org/10.1075/ts.18014.moo
Mossop, B. (2020). Revising and editing for translators. Routledge. https://doi.org/10.4324/9781315158990
O’Brien, S. (2005). Methodologies for measuring the correlations between post-editing effort and machine translatability. Machine Translation, 19(1), 37–58. https://doi.org/10.1007/s10590-005-2467-1
O’Brien, S. (2011). Towards predicting post-editing productivity. Machine Translation, 25(3), 197–215. https://doi.org/10.1007/s10590-011-9096-7
Pérez-González, L. (Ed.) (2018). The Routledge handbook of audiovisual translation. Routledge. https://doi.org/10.4324/9781315717166
Pérez Macías, L. (2020). ¿Qué piensan los traductores sobre la posedición?: Un estudio basado en el método mixto sobre los temores, las preocupaciones y las preferencias en la posedición de traducción automática. Tradumàtica, 18, 11–32. https://doi.org/10.5565/rev/tradumatica.227
Plitt, M., & Masselot, F. (2010). A productivity test of statistical machine translation post-editing in a typical localisation context. The Prague Bulletin of Mathematical Linguistics, 93, 7–16.
Robert, I., Ureel, J. J. J., & Schrijver, I. (2023). Translation, translation revision and post-editing competence models: Where are we now? In G. Massey, E. Huertas-Barros & D. Katan (Eds.), The human translator in the 2020s (pp. 44–59). Routledge. https://doi.org/10.4324/9781003223344-4
Romero-Fresco, P., & Chaume, F. (2022). Creativity in audiovisual translation and media accessibility. The Journal of Audiovisual Translation, 38, 75–101.
Rossi, C., & Chevrot, J.-P. (2019). Uses and perceptions of machine translation at the European Commission. The Journal of Specialised Translation, 31, 177–200.
Specia, L., & Farzindar, A. (2010). Estimating machine translation post-editing effort with HTER. In V. Zhechev (Ed.), Proceedings of the Second Joint EM+/CNGL Workshop Bringing MT to the User: Research on Integrating MT in the Translation Industry (pp. 33–41). Association for Machine Translation in the Americas. https://aclanthology.org/2010.jec-1.5
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Alejandro Bolaños García-Escribano, Jorge Díaz-Cintas
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under the CC BY-NC 4.0 Deed that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. The material cannot be used for commercial purposes.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
