Linguistica Antverpiensia, New Series – Themes in Translation Studies: Announcements

Call for abstracts (and papers): Call for abstracts & papers: Machine and Computer-assisted Interpreting - LANS-TTS issue 24, publication year 2025

Linguistica Antverpiensia, New Series – Themes in Translation Studies — 2023-09-05

Call for abstracts & papers: Machine and Computer-assisted Interpreting

LANS-TTS issue 24, publication year 2025

Guest editors

Lu Xinchao, Beijing Foreign Studies University (China)
Claudio Fantinuoli, Mainz University (Germany)

Lu Xinchao is the Director of the Center for Research on Interpreting Practice and Pedagogy (CRIPP), a platform boasting an interdisciplinary team of interpreting practitioners, trainers, researchers, and experts in natural language processing, computational linguistics, and machine translation, as well as machine interpreting engineers and machine interpreting system developers from leading companies. By engaging researchers from the interpreting community and beyond, we aim to explore the most relevant themes related to interpreting, ranging from interpreting competence, processes, and products to the interpreting profession and its associated pedagogy, with a special interest in technology-motivated research themes, such as machine interpreting.

Claudio Fantinuoli is researcher and lecturer at the Mainz University/Germany and Head of Innovation at KUDO Inc. He conducts research in the field of Natural Language Processing applied to computer-assisted interpreting and automatic speech translation. He also teaches conference interpreting. In the past, he taught Technology and Interpreting at the University of Innsbruck and at the Postgraduate Center of the University of Vienna. He is the founder of InterpretBank, an AI-based tool for conference interpreters.

Machine and computer-assisted interpreting

During the last decades, information technology has played a central role in the domain of spoken language translation. Currently, there are two major lines of research on this area: machine interpreting and computer-assisted interpreting.

Machine interpreting (automatic interpreting, speech translation, and speech-to-speech translation) refers to the practice, process, or product of real-time automatic or automated speech translation by a computerized system combining components of automatic speech recognition, machine translation, speech synthesis, and subtitling (Cho et al., 2013; Fügen et al., 2007; Horváthp, 2022; Müller et al., 2016; Stüker et al., 2012). It explores how machine systems perform interpreting, and studies in this field, which are most often conducted by scientists and engineers, deal with interpreting system design, improvement, and evaluation. The areas explored include interpreting system design (e.g., Fügen et al., 2007; Jekat, 1996; Luperfoy, 1996; Sakamoto et al., 2013; Wang et al., 2016); evaluation of machine interpreting quality (e.g., Hamon et al., 2009; Le et al., 2018; Stewart et al., 2018); improvement of interpreting rules or models (e.g., He et al., 2015; Siahbani et al., 2018); key issues and processes, such as sentence segmentation (e.g., Siahbani et al., 2018); incremental processing and latency (e.g., Fujita et al., 2013; Grissom II et al., 2014); and facial expression-based affective speech translation (Székely et al., 2014), corpus construction, and machine learning (e.g., Murata et al., 2010; Shimizu et al., 2013).

Unlike machine translation research and development, which spans over half a century, machine interpreting is an emerging field that is far less explored. Machine interpreting was first tested in the 1980s and implemented in the 1990s; by early this century, researchers and developers in fields such as computer science, linguistics, speech processing, and artificial intelligence made it possible for machine systems to interpret dialogues for reservations and scheduling, travel conversations, broadcast news, parliament speeches, improvised speeches, and lectures (Nakamura, 2009; Pöchhacker, 2015, pp. 239–242, 2016, p. 194; Waibel & Fügen, 2008). It was listed by the MIT Technology Review in 2004 as one of the “10 emerging technologies that will change your world.”

Computer-assisted interpreting tools are programs designed to support professional interpreters during the different phases of the interpreting workflows and encompass all tools that aims at integrating into the interpreter’s workstation the latest advances in Natural Language Processes and Artificial Intelligence. Among others, CAI tools support interpreters and interpreter managers in creating terminological resources, in managing and reusing event information for future tasks, in sharing such information among different stakeholders, to access information in real-time during the delivery of the interpreting service, and, more recently, to perform activities such as quality assurance and similar.

By leveraging automatic speech recognition, transcription and transcript display, and machine translation, and key technologies or components of machine interpreting systems, students and professional interpreters can improve their interpreting accuracy, particularly with numbers and terms (e.g., Defrancq & Fantinuoli, 2020; Desmet et al., 2018; Sun et al., 2021; Zhang et al., 2018; Fantinuoli, 2017).

In the last two decades, impressive progress in automatic speech recognition, natural language processing, artificial intelligence, deep learning, and neural machine translation has given a major boost to the development of machine and computer-assisted interpreting systems, improving their robustness with increasingly uncertain and diversified source language features and environments and extending their domains, modes, and scenarios of application through enhanced acceptability, affordability, portability, and usability. Machine systems, whether assisting human interpreters or working alone, are reshaping and will continue to reshape the global ecosystem of interpreting—its practices, processes, products, profession, and pedagogy.

With most of the existing literature being general introductions or theoretical explorations, there has been a dearth of empirical research (cf. Tripepi Winteringham, 2010; Fantinuoli, 2018; Oritz and Cavallo, 2019), and particularly of applied research, conducted by researchers, trainers, and practitioners in the interpreting community. Given this underrepresentation, many fundamental questions remain to be answered (cf. Mellinger, 2021; Prandi 2023).

What is the state of the art of machine and computer-assisted interpreting development? What are the major bottlenecks and challenges in developing quality machine interpreting systems? What are the latest developments and innovations in machine interpreting processes (e.g., from three components to end-to-end, multimodal information processing and integration)? How do machine systems compare to human interpreters in terms of interpreting competences, processes, and products? How do computer-assisted systems interact or collaborate with human interpreters? What are the potential areas (domains, modes, patterns, etc.) of machine–human complementarity? How do machine interpreting and computer-assisted interpreting tools redefine interpreters’ roles and competences while reshaping interpreting pedagogy and practice and the language industry as a whole? What are the potential risks or ethical issues related to machine interpreting?

To respond to these questions, we need to examine and explore certain key themes, including (but not limited to) the following:

machine interpreting system design (e.g., end-to-end interpreting systems; domain- or mode-specific vs. general interpreting systems; configurable systems adapted to communicative situations; working modes, domains, language combinations, and interpreting directions; and source language variables)
innovative models, processes, or mechanisms of machine interpreting (e.g., processing of disfluencies, prosodic, pragmatic, and visual information; and low latency/concurrency of processing)
interpreting corpus construction for machine learning
machine interpreting quality evaluation
evaluation of computer-assisted interpreting
cognitive implications of use of computer-assisted interpreting tools in simultaneous interpretation
use of computer-assisted interpreting tools in underexplored settings, such as liaison interpreting
comparison of human and machine interpreting competences
comparison of human and machine interpreting processes
comparison of human and machine interpreting products
machine-aided human interpreting (system design; operational procedures and mechanisms; and products and performances specific to different language combinations, event types, interpreting modes, domains, themes, source language variables, etc.)
human-aided machine interpreting (system design; operational procedures and mechanisms; and products and performances specific to different language combinations, event types, interpreting modes, domains, themes, source language variables, etc.)
advances of computer-assisted interpreting tools and their effectiveness
emerging forms of hybridization in the delivery of interpreting services
implications of machine and computer-assisted interpreting for the interpreting profession and the language industry (e.g., competition and collaboration between interpreters and machine systems, roles of machine systems and interpreters in linguistic/cultural mediation, new professional profiles and working conditions and remuneration)
implications of machine and computer-assisted interpreting for interpreter training
the ethics of artificial intelligence applied to machine interpreting and computer-assisted interpreting (e.g., training data bias and data quality, interpreting data ownership and privacy, and the transparency of machine system development and decision-making processes in machine interpreting)

Selected papers will be submitted for a double-blind peer review as requested by LANS–TTS.

Practical information and deadlines

Proposals: Please submit abstracts of approximately 500–1000 words in English, including relevant references (not included in the word count), to both Lu Xinchao (luxinchao@bfsu.edu.cn) and Claudio Fantinuoli (fantinuoli@uni-mainz.de) in the same email.

Abstract deadline: 1 April 2024
Acceptance of abstract proposals: 1 June 2024
Submission of papers: 1 November 2024
Acceptance of the papers: 1 March 2025
Submission of final versions of papers: 1 June 2025
Editorial work (proofreading and APA check): June to November 2025
Publication: December 2025

For all submissions (abstracts and full papers), authors have to use APA 7^th.

References (apa.org)
APA Style Reference Guide for Journal Articles, Books, and Edited Book Chapters, APA Style 7th Edition
APA Style Common Reference Examples Guide, APA Style 7th Edition

References (APA 7th edition).

Cho, E., Fugen, C., Herrmann, T., Kilgour, K., Mediani, M., Mohr, C., Niehues, J., Rottmann, K., Saam, C., Stuker, S., & Waibel, A. (2013). A real-world system for simultaneous translation of German lectures. INTERSPEECH, 13, 3473–3477. https://doi.org/10.21437/Interspeech.2013-612

Defrancq, B., & Fantinuoli, C. (2020). Automatic speech recognition in the booth: Assessment of system performance, interpreters’ performances and interactions in the context of numbers. Target, 33 (1), 73–102. https://doi.org/10.1075/target.19166.def

Desmet, B., Vandierendonck, M., & Defrancq, B. (2018). Simultaneous interpretation of numbers and the impact of technological support. In C. Fantinuoli (Ed.), Interpreting and Technology, 13–27. Language Science Press.

Fantinuoli, C. (2018). Interpreting and technology: The upcoming technological

turn. In C. Fantinuoli (Ed.), Interpreting and Technology, 1–12. Language Science Press.

Fantinuoli, C. (2017). Speech Recognition in the Interpreter Workstation. Proceedings of the Translating and the Computer, 39, 25–34.

Fügen, C., Waibel, A., & Kolss, M. (2007). Simultaneous translation of lectures and speeches. Machine Translation, 21 (4), 209–252. https://doi.org/10.1007/s10590-008-9047-0

Fujita, T., Neubig, G., Sakti, S., Toda, T., & Nakamura, S. (2013). Simple, lexicalized choice of translation timing for simultaneous speech translation. INTERSPEECH, 13, 3487–3491. https://doi.org/10.21437/Interspeech.2013-615

Grissom II, A. C., Boyd-Graber, J., He, H., Morgan, J., & Daumé III, H. (2014). Don’t until the final verb wait: Reinforcement learning for simultaneous machine translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1342–1352. https://doi.org/10.3115/v1/D14-1140

Hamon, O., Fügen, D., Mostefa, D., Arranz, V., Kolss, M., Waibel, A., & Choukri, K. (2009). End-to-end evaluation in simultaneous translation. Proceedings of the 12th Conference of the European Chapter of the ACL, 345–353. https://doi.org/10.3115/1609067.1609105

Horváthp, I. (2022). AI in interpreting: Ethical considerations. Across Languages and Cultures, 23 (1), 1–13. https://doi.org/10.1556/084.2022.00108

He, H., Grissom II, A., Boyd-Graber, J., & Daumé III, H. (2015). Syntax-based rewriting for simultaneous machine translation. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 55–64. https://doi.org/10.18653/v1/D15-1006

Jekat, S.J., & Klein, A. (1996). Machine interpretation open problems and some solutions. Interpreting, 1 (1), 7–20. https://doi.org/10.1075/intp.1.1.02jek

Le, N., Lecouteux, B., & Besacier, L. (2018). Automatic quality estimation for speech translation using joint ASR and MT features. Machine Translation, 32 (4), 325–351. https://doi.org/10.1007/s10590-018-9218-6

Luperfoy, S. (1996). Machine interpretation of bilingual dialogue. Interpreting, 1 (2), 213–233. https://doi.org/10.1075/intp.1.2.03lup

Mellinger, C.D. (2019). Computer-assisted interpreting technologies: a product and process-oriented perspective. Revista Tradumàtica, 17, 33–44.

Müller, M., Nguyen, T.S., Niehues, J., Cho, E., Krüge, B., Ha, T.L., Kilgour, K., Sperber, M., Mediani, M., Stüker, S., & Waibel, A. (2016). Speech translation framework for simultaneous lecture translation. Proceedings of NAACL-HLT, 82–86. https://doi.org/10.18653/v1/N16-3017

Murata, M., Ohno, T., Matsubara, S., & Inagaki, Y. (2010). Construction of chunk-aligned bilingual lecture corpus for simultaneous machine translation. Proceedings of the International Conference on Language Resources and Evaluation, LREC 2010.

Nakamura, S. (2009). Overcoming the language barrier with speech translation technology. Quarterly Review, 31, 35–48.

Ortiz, L., & Cavallo, P. (2018). Computer-Assisted Interpreting Tools (CAI) and options for automation with Automatic Speech Recognition. TradTerm, 32, 9–31. https://doi.org/10.11606/issn.2317-9511.v32i0p9-31

Prandi, B. (2023). Computer-assisted simultaneous interpreting: A cognitive-experimental study on terminology. Science Language Press.

Pöchhacker, F. (2015). Routledge Encyclopedia of Interpreting Studies. Routledge. https://doi.org/10.4324/9781315678467

Sakamoto, A., Watanabe, N., Kamatani, S., & Sumita, K. (2013). Development of a simultaneous interpretation system for face-to-face services and its evaluation experiment in real situation. Proceedings of the XIV Machine Translation Summit, 85–92.

Shimizu, H., Neubig, G., Sakti, S., Toda, T., & Nakamur, S. (2013). Constructing a speech translation system using simultaneous interpretation data. Proceedings of the 10th International Workshop on Spoken Language Translation, 212–218.

Siahbani, M., Shavarani, H.S., Alinejad, A., & Sarkar, A. (2018). Simultaneous translation using optimized segmentation. Proceedings of AMTA 2018 (1): MT Research Track, 154–167.

Stewart, C., Vogler, N., Hu, J.J., Boyd-Graber, J., & Neubig, G. (2018). Automatic estimation of simultaneous interpreter performance. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Short Papers), 662–666. https://doi.org/10.18653/v1/P18-2105

Stüker, S., Herrmann, T., Kolss, M., Niehues, J., & Wölfel, M. (2012). Research opportunities in automatic speech-to-speech translation. IEEE Potentials, 31 (3), 26–33. https://doi.org/10.1109/MPOT.2011.2178192

Sun, H., Li, K., & Lu, J. (2021). AI-assisted simultaneous interpreting - An experiment and its Implications. Computer-assisted Foreign Language Education in China, 6, 75-80+86+12.

Székely, É., Steiner, I., Ahmed, Z., & Carson-Berndsen, J. (2014). Facial expression-based affective speech translation. Journal on Multimodal User Interfaces, 8 (1), 87–96. https://doi.org/10.1007/s12193-013-0128-x

Tripepi Winteringham, S. (2010). The usefulness of ICTs in interpreting practice. The Interpreters’ Newsletter, 15, 87–99.

Waibel, A., & Fügen, C. (2008). Spoken language translation enabling cross-lingual human–human communication. IEEE Signal Processing Magazine, May, 70–79. https://doi.org/10.1109/MSP.2008.918415

Wang, X. L., Finch, A., Utiyama, M., & Sumita, E. (2016). An efficient and effective online sentence segmenter for simultaneous interpretation. Proceedings of the 3rd Workshop on Asian Translation, 139–148.

Zhang, A., Yang, Z., Liu, C., & Li, S. (2018). A tentative proposal for translation ＆ interpreting based on human-computer collaboration through developments in artificial intelligence. Computer-Assisted Foreign Language Education in China, 3, 88–94.

Call for abstracts (and papers): Call for abstracts & papers (in English, Spanish, French and German): Translation for Social Justice: Concepts, Policies and Practices across Modalities and Contexts

Linguistica Antverpiensia, New Series – Themes in Translation Studies — 2023-01-16

Call for abstracts & papers (in English, Spanish, French and German): Translation for Social Justice: Concepts, Policies and Practices across Modalities and Contexts

Call for abstracts & papers - LANS-TTS Issue 23, publication year 2024

Guest editors

Dr Julie Boéri, Hamad Bin Khalifa University (Qatar)

Dr Ting Guo, University of Liverpool (UK)

Julie Boéri is Associate Professor in Translation, Interpreting and Intercultural Studies at Hamad Bin Khalifa University. She holds a PhD in Translation and Intercultural Studies from the University of Manchester. She has interpreted and/or coordinated interpreting in many social justice initiatives in Europe and Latin America. Her work focuses on the translational nature of contemporary social movements and civil society, and on the ethics of translation, interpreting and mediation. She co-edited (with Carol Maier, Kent State University, USA) the bilingual English and Spanish book Compromiso Social y Traducción/Interpretación – Translation/Interpreting and Social Activism. She has published her work in varying outlets: The Translator (Taylor & Francis), Translation and Interpreting Studies (John Benjamins), Quaderns, Puentes, The Translator and Interpreter Trainer, Meta: journal des traducteurs, Hermès, Language and Communication, Revues des Sciences de l’Information et de la Communication, among others. She has regularly contributed to Routledge Handbooks and Encyclopedia (on citizen media, translation, interpreting, ethics). She is the Vice-President of IATIS (International Association of Translation and Intercultural Studies).

Ting Guo is a Senior Lecturer in Translation and Chinese studies at University of Liverpool. She holds a PhD in Translation Studies (Aston University, UK). Her research focuses on the pivotal role of translators in the reproduction and dissemination of knowledge as well as in cultural and social changes. She has coedited two special issues on the topic of queer translation, with Michela Baldo (University of Birmingham) and Jonathan Evans, of Perspectives: Studies in Translation Theory and Practice (in press 2023) and Translation and Interpreting Studies (published 2021). Ting publishes widely in international journals such as Translation Studies and Literature Compass, and she is the author of Surviving Violent Conflict: Chinese Interpreters in the Second-Sino Japanese War (1931-45) (2016). She is the Associate Editor of Target, the International Journal of Translation Studies and member of the Advisory Board of Translation in Society as well as member of the Advisory Panel of New Voices in Translation Studies.

Translation for Social Justice: Concepts, Policies and Practices across Modalities and Contexts

The transnational nature of contemporary movements, media and networks in our globalized and interconnected societies has placed translation at the heart of counter-hegemonic discourses and endeavours. In this context, translation has become a powerful prism through which to think and practice social justice. Although largely intellectualized in relation to Western, liberal welfare states, social justice is also a performative and interpersonal prism of social change (Sen, 2009), with roots historically spread across cultures, traditions and territories, and with ramifications in contemporary forms of resistance, including struggles for the rights of humans but also of animals and nature. Thus, while social justice has traditionally been understood as the fair distribution of means and resources and the recognition of people’s rights across status in a given society (Fraser & Honneth, 2003), the increased interconnection of struggles across the world has broadened social justice in ways that heighten the stakes of translation. The leverage and enactment of the multiple rights which social justice now encompasses is contingent upon the organization, the practice and the theorization of translation (Boéri, 2022) in all its modalities (translation, interpreting, bilingual facilitation, fixing, subtitling, dubbing) and across communication contexts of resistance (social movements, media networks, cultural institutions).

Combining a translational focus on social justice and a social justice focus on translation can harness the political and ethical potentials of this area of enquiry and practice, emerging from the liminal space between activism and the service economy (Baker, 2013; Boéri, 2008, 2012; Boéri & Delgado Luchner, 2021; Piróth & Baker, 2020; Pérez-González, 2010, 2016), social justice and social movements (de Sousa Santos, 2005; Doerr, 2018; Fernández, 2021), social justice and public policy (García-Beyaert, 2017), social justice and art (Boéri, 2020), social justice and education (Bahadır, 2011; Boéri & Jerez, 2011; Gill & Guzmán, 2011), and social justice and gender equality ( Baldo et al., 2021; Guo, 2021; Spurlin, 2018). On the one hand, a translational approach to social justice invites scholars to account for the counter-hegemonic potential of cross-language communication, which tends to be overlooked in an all too often monolingual account of multilingual processes and spaces of resistance. On the other hand, a social justice focus on translation can yield powerful insights into the agency of the translation actors as dynamic/innovative agents in the performance of their duties, who may depart from and rethink deontological principles of impartiality and expertise. These two complementary and overlapping standpoints have the potential to renew our understanding of how social actors (including translators and interpreters) think and perform social justice beyond the monolingual and expert paradigms.

Bringing together studies from across contexts, regions and territories of resistance, this special issue aims to advance knowledge of the challenges and the stakes of overcoming language barriers in social justice endeavours. We seek submissions across translation and interpreting studies, with particular interest in interdisciplinary perspectives which can cast a critical light onto the social justice stakes of translation across contexts and modalities. Topics of interest include but are not limited to:

The politics of organization of cross-language communication in past and contemporary social justice endeavours across contexts (movements, media, cultural institutions)
Framing and leveraging translation/interpreting for social justice: stakes, challenges and levers in and beyond liberal democracies
Enacting social justice in adversarial and collaborative cross-language encounters: positionality, ethics, constraints and agency
The translation labor of social justice: wages, volunteering, working conditions, expertise, skills, affect
Individual and collective trajectories of social justice actors: processes of collective identity formation among activists who translate and activist translators
Translation/Interpreting pedagogies of social justice: curriculum developments in ad hoc, community and formal training
Epistemologies of translational counter-hegemonic endeavors: revisiting and renewing concepts, methods, frameworks, models and paradigms for social justice

Selected papers will be submitted for a double-blind peer review as requested by LANS–TS.

Practical information and deadlines

Proposals: Please submit abstracts of approximately 500–1000 words in English, French, Spanish or German, including relevant references (not included in the word count), to both Dr Julie Boéri (jboeri@hbku.edu.qa) and Dr Ting Guo (ting.guo@liverpool.ac.uk) in the same email.

Abstract deadline: 1 April 2023
Acceptance of abstract proposals: 1 June 2023
Submission of papers: 1 November 2023
Acceptance of the papers: 1 March 2024
Submission of final versions of papers: 1 June 2024
Editorial work (proofreading and APA check): June to November 2024
Publication: December 2024

For all submissions (abstracts and full papers), authors have to use APA 7^th.

References (apa.org)
APA Style Reference Guide for Journal Articles, Books, and Edited Book Chapters, APA Style 7th Edition
APA Style Common Reference Examples Guide, APA Style 7th Edition

References

Bahadır, Ş. (2011). Interpreting enactments: a new path for interpreting pedagogy. In C. Kainz, E. Prunc, & R. Schögler (Eds.), Modelling the field of community interpreting: Questions of methodology in research and training (pp. 177–210). LIT Verlag.

Baker, M. (2013). Translation as an alternative space for political action. Social Movement Studies, 12(1), 23–47. https://doi.org/10.1080/14742837.2012.685624

Baldo, M., Evans, J., & Guo, T. (2021). Introduction: translation and LGBT+/queer activism. Translation and Interpreting Studies, 16(2), 185–195. https://doi.org/10.1075/tis.00051.int

Boéri, J. (2008). A narrative account of the Babels vs. Naumann controversy. The Translator, 14(1), 21–50. https://doi.org/10.1080/13556509.2008.10799248

Boéri, J. (2020). Diversity. In M. Baker, L. Pérez González, & B. B. Blaagaard (Eds.), Routledge encyclopedia of citizen media (pp. 140–145). Routledge. https://doi.org/10.4324/9781315619811

Boéri, J. (2022). Steering ethics towards social justice: A model for a meta-ethics of interpreting. Translation and Interpreting Studies: https://doi.org/10.1075/tis.20070.boe

Boéri, J., & Jerez, J. D. M. (2011). From training skilled conference interpreters to educating reflective citizens. The Interpreter and Translator Trainer, 5(1), 27–50. https://doi.org/10.1080/13556509.2011.10798811

de Sousa Santos, B. (2005). The future of the world social forum: The work of translation. Development, 48(2), 15–22. https://doi.org/10.1057/palgrave.development.1100131

Doerr, N. (2018). Political translation: How social movement democracies survive. Cambridge University Press.

Fraser, N., & Honneth, A. (2003). Redistribution or recognition? A political philosophical exchange. Verso.

García-Beyaert, S. (2017). Public concern, public policy and PSI: The public dimension of language interpreting. Revista Canaria de Estudios Ingleses, 75, 15–29.

Gill, Rosalind M. & Guzmán, M. C. (2011). Teaching translation for social awareness in Toronto. The Interpreter and Translator Trainer (ITT), 5(1), 93–108. https://doi.org/10.1080/13556509.2011.10798813

Guo, T. (2021). ‘Love is love’ and ‘Love is equal’: Translation and queer feminism in China. In M. Bracke, J. Bullock, P. Morris, & K. Schulz (Eds.) Translating feminism (pp. 199–226). Palgrave.

Pérez-González, L. (2010). ‘Ad-hocracies’ of translation activism in the blogosphere: A genealogical case study. In M. Baker, M. Olohan, & M. Calzada Pérez (Eds.), Text and context essays on translation and interpreting in honour of Ian Mason (pp. 259–287). St Jerome Publishing. https://doi.org/10.4324/9781315759739

Pérez-González, L. (2016). The politics of affect in activist amateur subtitling: A biopolitical perspective. In M. Baker & B. Blaagaard (Eds.), Citizen media and public spaces: Diverse expressions of citizenship and dissent (pp. 118–135). Routledge. https://doi.org/10.4324/9781315726632

Piróth, A., & Baker, M. (2020). Volunteerism in translation: Translators without borders and the platform economy. In E. Bielsa & D. Kapsaskis (Eds.), The routledge handbook of translation and globalization (pp. 406–424). Routledge. https://doi.org/10.4324/9781003121848

Sen, A. (2009). The idea of justice. Harvard University Press.

Spurlin, W. (2018). Queering translation: Rethinking gender and sexual politics in the spaces between languages and culture. In B. J. Epstein and R. Gillett (Eds.), Queer in translation (pp. 172–183). Routledge. https://doi.org/10.4324/9781315603216

Call for abstracts (and papers): Call for abstracts (& papers): The impact of Machine Translation in the Audiovisual Translation environment: professional and academic perspectives

Linguistica Antverpiensia, New Series – Themes in Translation Studies — 2022-01-21

The impact of Machine Translation in the Audiovisual Translation environment: professional and academic perspectives

Call for abstracts & papers - Issue 22, publication year 2023

The impact of Machine Translation in the Audiovisual Translation environment: professional and academic perspectives

Guest editors

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 (delosrey@uji.es) and Laura Mejías-Climent (lmejias@uji.es).

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

References

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