[CFP] The impact of Machine Translation in the Audiovisual Translation environment: professional and academic perspectives
Issue 22, publication year 2023
The impact of Machine Translation in the Audiovisual Translation environment: professional and academic perspectives
Dr. Julio de los Reyes Lozano1
Dr. Laura Mejías-Climent1
1Universitat Jaume I, Spain
Julio de los Reyes Lozano is a full-time lecturer and researcher of the Department of Translation and Communication at Universitat Jaume I, Spain. He holds a PhD in Translation Studies from the Universities Jaume I, Spain, and Reims-Champagne-Ardenne, France. He has published several articles in prestigious journals in the area of Translation Studies and book chapters in well-known publishers. He is co-author of a monograph on subtitling (UJI, 2019) and co-editor of a collection of essays on AVT (L’Entretemps, 2021).
Laura Mejías-Climent is a full-time lecturer and researcher of the Department of Translation and Communication and Universitat Jaume I, where she completed her PhD in Translation Studies. She has published articles on audiovisual translation and localization in prestigious journals such as MonTi, LANS, Trans, Sendebar, among others, as well as chapters of several books with leading publishers. She is also the author of a recently-published book on localization with the prestigious publisher Palgrave Macmillan.
Dr. De los Reyes Lozano and Dr. Mejías-Climent are the main researchers of the project entitled DubTA: La traducción automática aplicada a los procesos de traducción para doblaje [the application of machine translation to the dubbing process], funded by the Universitat Jaume I over the period 2021-2022 (ref. UJI-B2020-56).
The impact of machine translation in the audiovisual translation environment: professional and academic perspectives
Interest in Machine Translation (MT) and post-editing (PE) is coming on apace: the arrival in 2017 of new translation services based on artificial intelligence algorithms such as DeepL, Microsoft Translate and Google Translate represented a new leap forward, and an increasing number of translation fields are incorporating MT and PE into their professional environment. These newer systems use artificial neural networks (NMT or neural machine translation) and, as the previous generation of MT (rule-based, statistical, example-based, and hybrid), work with large aligned corpora and produce results that some may consider comparable to certain human translations. It so happens that in order to produce an added value, the translator must provide something extra over the machine.
MT and PE are also gradually beginning to intersect with some fields where the use of the machine is of little interest because of the essentially aesthetic dimension of translation (e.g. Literary Translation, Comic Translation, Video Games Localization, Transcreation or Audiovisual Translation, among others). In the particular case of Audiovisual Translation (AVT), MT has traditionally remained distant due to the difficulty of fully processing the information generated by the audiovisual text: as a multimodal product, not only linguistic content is involved, but also the visual and acoustic configuration of the product must be taken into account. This happens in all AVT modes (dubbing, subtitling, audio description, subtitling for the deaf and the hard of hearing, respeaking, etc.). Likewise, the huge variety of audiovisual genres without domain-specific terminology makes the work of MT engines even more difficult. In addition, it is also very difficult to process dialogues within the soundtrack of the audiovisual text, in which many different characters participate, there are distant voices or sound effects, noises, etc. This means that, on many occasions, the scripts do not correspond exactly with the script of the final product.
It has been estimated that the use of MT allows notable productivity gains at least partly, on specific conditions (some translators achieve outputs of 3,000 to 9,000 words per day) (Zhechev, 2012). The PE process is becoming increasingly popular in the language industries, as confirmed in 2017 by the publication of the ISO 18587:2017 standard (Translation services – Post-editing of machine translation output – Requirements). This PE technique poses a case of conscience for the translator: accepting that he or she is not the originator of his or her own translation for the benefit of the machine. Among other aspects, this new situation involves a number of ethical issues, such as the client explicitly informing the translator that the text he/she will be working with represents raw MT results, as the ISO norm states, or the way confidentiality is approached when the material is processed by freely-available MT engines, to name a but a few. These issues, although widely explored in other areas, have been scarcely researched thus far in the particular field of AVT.
Furthermore, in recent months, the professional world has been expressing different positions towards the imposition that some companies seem to be making of MT in the AVT environment. On the one hand, the Machine Translation Manifesto published by AVTE (Audiovisual Translators Europe) in 2021 shows a critical but constructive stance towards the entrenchment of this new technology as another tool that can be adapted to the translators’ needs. On the other hand, ATRAE and ATAA (the associations of audiovisual translators in Spain and France, respectively) have issued statements on their social media censuring the use of MT in AVT and considering it dangerous and demeaning to the work of the human translator, following the controversy generated by the fact that the Spanish subtitles of the popular Netflix show “The Squid Game” were created by post-editing. The debate on this controversy is open and may give rise to many avenues of research.
Bearing this current context in mind, it is worth exploring how the incorporation of MT into the translation processes is affecting the professional spheres, and how the academic circles are broadening their knowledge of MT. We invite original, substantial, and unpublished research in all aspects of MT converging with the professional and academic environment of AVT in any mode. We seek submissions across the entire spectrum of MT/AVT-related research, but with a particular focus on the close interaction between researchers and practitioners who are looking to apply the latest MT technology to their tasks. Topics of interest include but are not limited to:
- MT in AVT modes (dubbing, subtitling, accessibility) and products (films, series, video game localization…), including case studies
- Translation quality, models of evaluation of MT and PE in AVT
- Productivity evaluation in automated AVT
- Professional practices of MT and the role of new technologies in AVT
- New work environments: AVT and MT in the cloud
- The use of human feedback to improve MT in AVT: ethical and professional issues
- The role of the audiovisual translator in the MT era: rights, demands and concerns
- MT in specific audiovisual genres
- MT for multimedia communication (chats, blogs, social networks)
- Benefits and limits of MT in specific domains of AVT
- Creativity and MT: the importance of context in AVT
- MT for “non-standard” language in films and TV series
- The language of dubbing: dubbese and MT
- Gender issues in MT and AVT
- MT for minority languages and low resource languages in AVT
- MT and PE in the AVT classroom
- Language acquisition through AVT and MT
Selected papers will be submitted to a double-blind peer review as requested by LANS-TTS.
Practical information and deadlines
Proposals: Please submit abstracts of approximately 500 words, including relevant references (not included in the word count), to both Julio de los Reyes Lozano (email@example.com) and Laura Mejías-Climent (firstname.lastname@example.org).
- Abstract deadline: 1 April 2022
- Acceptance of abstract proposals: 1 June 2022
- Submission of papers: 1 November 2022
- Acceptance of papers: 28 February 2023
- Submission of final versions of papers: 1 June 2023
- Editorial work (proofreading, APA, layout): June-November 2023
- Publication: December 2023
AVTE (2021). Machine Translation Manifesto. Retrieved from http://avteurope.eu/wp-content/uploads/2021/09/Machine-Translation-Manifesto_ENG.pdf
Cadwell, P., O’Brien, S. & Teixeira, C. S. C. (2017). Resistance and Accommodation: Factors for the (Non-) Adoption of Machine Translation among Professional Translators. Perspectives, 26(3), 301–321. https://doi.org/10.1080/0907676X.2017.1337210
Cid-Leal, P., Espín-García, M. C. & Presas, M. (2019). Traducción automática y posedición: perfiles y competencias en los programas de formación de traductores. MonTI. Monografías de Traducción e Interpretación, 11, 187-214. https://doi.org/10.6035/monti.2019.11.7
Federico, M., Enyedi, R., Barra-Chicote, R., Giri, R., Isik, U., Krishnaswamy, A. & Sawaf, H. (2020). From Speech-to-Speech Translation to Automatic Dubbing. ArXiv. Retrieved from https://arxiv.org/abs/2001.06785
Fernández-Torné, A. & Matamala, A. (2015). Text-to-Speech vs. Human Voiced Audio Descriptions: A Reception Study in Films Dubbed into Catalan. The Journal of Specialised Translation, 24, 61-88.
Georgakopoulou, P. (2019). Technologization of Audiovisual Translation. En L. P. González (Ed.), The Routledge Handbook of Audiovisual Translation (pp. 516-539). Nueva York, Estados Unidos: Routledge.
International Organization for Standardization. (2017). Translation services – Post-editing of machine translation output – Requirements (ISO 18587:2017). Retrieved from https://www.iso.org/standard/62970.html
Jiménez-Crespo, M. A. (2020). The “Technological Turn” in Translation Studies. Are we there yet? A transversal cross-disciplinary approach. Translation Spaces, 9(2), 314-341. https://doi.org/10.1075/ts.19012.jim
Karakanta, A., Bhattacharya, S., Nayak, S., Baumann, T., Negri, M., & Turchi, M. (2020). The Two Shades of Dubbing in Neural Machine Translation. Proceedings of COLING – 28th International Conference on Computational Linguistics, 4327-4333. 10.18653/v1/2020.coling-main.382
Koponen, M. & Salmi, L. (2015). On the Correctness of Machine Translation: A Machine Translation Post-Editing Task. The Journal of Specialised Translation, 23, 118-136. https://jostrans.org/issue23/art_koponen.pdf.
Loock, R. (2020). No more rage against the machine: how the corpus-based identification of machine-translationese can lead to student empowerment. The Journal of Specialised Translation, 34, 150-170.
Matousek, J. & Vít, J. (2012). Improving Automatic Dubbing with Subtitle Timing Optimisation Using Video Cut Detection. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Retrieved from http://www.kky.zcu.cz/en/publications/MatousekJ_2012_ImprovingAutomatic.
Matusov, E., Wilken, P. & Georgakopoulou, Y. (2019). Customizing Neural Machine Translation for Subtitling. Proceedings of the Fourth Conference on Machine Translation (WMT), 1, 82-93. https://doi.org/10.18653/v1/w19-5209
Moorkens, J. (2018). What to expect from Neural Machine Translation: a practical in-class translation evaluation exercise. The Interpreter and Translator Trainer, 12(4), 375-387.
Nunes Vieira, L. (2020). Post-editing of machine translation. In M. O’Hagan (Ed.), The Routledge Handbook of Translation and Technology (pp. 319–335). Routledge.
Sánchez Ramos, M. M. & Rico Pérez, C. (2020). Traducción Automática. Conceptos clave, procesos de evaluación y técnicas de posedición. Granada: Comares.
Zhechev, V. (2012). Machine Translation Infrastructure and Post-editing Performance at Autodesk. AMTA 2012 Workshop on Post-Editing Technology and Practice (WPTP 2012), 87-96. https://aclanthology.org/2012.amta-wptp.10.pdf