Article published In: Intelligences pour la traduction. IA et interculturel : actions et interactions.
Edited by Ludovica Maggi and Sarah Bordes
[FORUM 20:2] 2022
► pp. 357–367
Neural machine translation and the indivisibility of culture and language
Published online: 12 January 2023
https://doi.org/10.1075/forum.00025.san
https://doi.org/10.1075/forum.00025.san
Abstract
Neural machine translation (NMT) is becoming a common resource both for professional translators and for people who need small, occasional translations. The wide use of NMT for professional purposes reshapes the conditions of translation assignments. Moreover, the effects on the target text and language are not well known, although some studies already suggest a stronger influence of the source language on them. On the other hand, since many NMT tools are easily and freely available and even accessible via mobile devices, they are being increasingly used by non-professionals to carry out short translations mostly. These occasional translators use these tools both to understand a text originally written in a language they do not fluently speak (translation into their first language) and to publish a text in the language they do not master (translation into their second language or even into a language in which the user has poor or no knowledge at all). Finally, NMT is also present in everyday digital products even without users being aware of its intervention, for example in specific apps on smart devices. This article proposes a reflection on the effects of NMT in all these scenarios, with a special focus on the effects on the reception of the target text and on the target language standard.
Résumé
Alors que la traduction automatique neuronale (NMT) devient une ressource courante, l’utilisation généralisée de cet outil technologique modifie les conditions d’exercice de la traduction. D’une part, les effets de la NMT sur le texte et sur la langue cibles n’ont pas été précisément identifiés, bien que certaines études suggèrent que dans le cadre de la NMT, l’influence de la langue source sur le produit cible est plus marquée que dans la traduction humaine. D’autre part, l’accessibilité à titre gratuit de nombreux outils de NMT, y compris à partir des appareils mobiles, ouvre la pratique de la traduction à des traducteurs non professionnels. Ces derniers se servent occasionnellement de la NMT pour comprendre des textes, le plus souvent courts, écrits dans une langue qu’ils ne maîtrisent pas (traduction vers la langue maternelle) ou pour publier des textes dans une langue qu’ils maîtrisent peu ou pas du tout (traduction vers une langue étrangère). Enfin, la NMT est présente dans les produits numériques du quotidien, tels que de nombreuses applications pour smartphone, sans même que les utilisateurs en aient conscience. Cet article propose une réflexion sur les effets de la NMT dans l’ensemble des contextes évoqués et souligne les impacts de la traduction automatique neuronale sur la réception du texte cible et sur la norme de la langue cible.
Article outline
- 1.Introduction
- 2.Data in Neural Machine Translation
- 3.What translation has already taught us
- 4.Moving towards a target language-and-culture friendly model
- 5.Concluding remarks
- Acknowledgements
References
References (22)
Ayoub, Kareem & Payne, Kenneth. 2016. “Strategy in the Age of Artificial Intelligence”,. Journal of Strategic Studies, 391:5–6, 793–819. < > [Last accessed: 04.08.2022].
Baker, Mona. 1993. “Corpus linguistics and translation studies: Implications and applications”. In M. Baker, G. Francis, & E. Tognini-Bonelli (eds.), Text and technology: In honour of John Sinclair, 233–252. Amsterdam: John Benjamins.
Bowker, Lynne. 2009. “Can Machine Translation meet the needs of official language minority communities in Canada? A recipient evaluation”. Linguistica Antverpiensia, New Series-Themes in Translation Studies 81, 123–157. Antwerp: Artesis University College.
Castilho, Sheila; Moorkens, Joss; Gaspari, Federico; Calixto, Iacer; Tinsley, John & Way, Andy. 2017. “Is neural machine translation the new state of the art?”. The Prague Bulletin of Mathematical Linguistics 108.11, 109–120. < > [Last accessed: 04.08.2022].
Daems, Joke; De Clercq, Orphée, & Macken, Lieve. 2017. “Translationese and post-editese: How comparable is comparable quality?”. Linguistica Antverpiensia, New Series-Themes in Translation Studies, 161, 89–103 <[URL]> [Last accessed: 04.08.2022].
Edunov, Sergey; Myle, Ott; Ranzato, Marc’Aurelio & Auli, Michael. 2019. “On The Evaluation of Machine Translation Systems Trained With Back-Translation”. Retrieved from arXiv preprint, Cornell University. <[URL]> [Last accessed: 15.04.2022].
Fiederer, Rebecca & O’Brien, Sharon. 2009. “Quality and machine translation: A realistic objective”. The journal of Specialised translation 11.111, 52–74. <[URL]> [Last accessed: 04.08.2022].
Forcada, Mikel L. 2017. “Making sense of neural machine translation”. Translation spaces 6.21, 291–309. < > [Last accessed: 04.08.2022].
Frederking, Robert; Mariani, Joseph & Zampolli, Antonio. 1999. Multilingual Information Management: Current Levels and Future Abilities. <[URL]> [Last accessed: 04.08.2022].
Freitag, M., Caswell, I., & Roy, S. 2019. APE at Scale and its Implications on MT Evaluation Biases. Fourth Conference on Machine Translation (WMT), 34–44. Retrieved from arXiv preprint, Cornell University. <[URL]> [Last accessed: 04.08.2022].
Fritzala, Panagiota. 2022. “A Case Study on the Effectiveness of NMT for Marketing Texts”. New Trends in Translation and Technologies (NeTTT 2022). Rhodes, 4–6 July, 2022. <[URL]> [Last accessed: 04.08.2022].
Gar Bailo, Leire & Sánchez-Gijón, Pilar. 2022. “Approaching NMT from the perspective of human translation techniques. What are the differences?”. New Trends in Translation and Technologies (NeTTT 2022). Rhodes, 4–6 July, 2022. <[URL]> [Last accessed: 04.08.2022].
Junczys-Dowmunt, Marcin. 2019. “Microsoft translator at WMT 2019: Towards large-scale document-level neural machine translation”. Retrieved from arXiv preprint, Cornell University. [Last accessed: 15.04.2022].
Kersting, K., Meyer, U. 2018. “From Big Data to Big Artificial Intelligence?”. Künstl Intell 321, 3–8. < > [Last accessed: 04.08.2022].
Large, Duncan. 2018. “Could Google Translate Shakespeare?”. In Other Words 521, 79–98. <[URL]> [Last accessed: 04.08.2022].
Moorkens, Joss, et al. 2018. “Translators’ perceptions of literary post-editing using statistical and neural machine translation”. Translation Spaces 7.21, 240–262. < > [Last accessed: 04.08.2022].
O’Brien, Sharon. 2022a. “Augmented Translation: New Trend, Future Trend or Just Trendy?”. New Trends in Translation and Technologies (NeTTT 2022). Rhodes, 4–6 July, 2022. <[URL]> [Last accessed: 04.08.2022].
. 2022b. “How to deal with errors in machine translation: Postediting”. In Dorothy Kenny (ed.), Machine translation for everyone: Empowering users in the age of artificial intelligence, 105–120. Berlin: Language Science Press. < > [Last accessed: 04.08.2022].
Rossi, Caroline & Carré, Alice. 2022. “How to choose a suitable neural machine translation solution: Evaluation of MT quality”. In Dorothy Kenny (ed.), Machine translation for everyone: Empowering users in the age of artificial intelligence, 51–79. Berlin: Language Science Press. <> [Last accessed: 04.08.2022].
Toral, Antonio. 2019. “Post-editese: an Exacerbated Translationese”. Retrieved form arXiv preprint, Cornell University. <> [Last accessed: 15.04.2022].
Unesco, Ich. 2020. “Basic texts of the 2003 convention for the safeguarding of the intangible cultural heritage”. <[URL]> [Last accessed: 15.04.2022].
Vasiļjevs, Andrejs; Skadina, Inguna; Sāmīte, Indra; Kauliņš, Kaspars; Ajausks, Ēriks; Meļņika, Jūlija & Bērziņš, Aivars. 2019. “Competitiveness Analysis of the European Machine Translation Market”. Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks. <[URL]> [Last accessed: 04.08.2022].
Cited by (1)
Cited by one other publication
Martín de Santa Olalla Sánchez, Aurora
This list is based on CrossRef data as of 9 december 2025. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.
