1887
  1. Home
  2. e-Journals
  3. FORUM. Revue internationale d’interprétation et de traduction / International Journal of Interpretation and Translation
  4. Volume 20, Issue 2
  5. Article
Volume 20, Issue 2
  • ISSN 1598-7647
  • E-ISSN: 2451-909X
USD
Buy:$35.00 + Taxes

Abstract

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.

© John Benjamins Publishing Company
Loading

Article metrics loading...

/content/journals/10.1075/forum.00025.san
2023-01-12
2024-10-08

  • From This Site

    /content/journals/10.1075/forum.00025.san
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Ayoub, Kareem & Payne, Kenneth
    2016 “Strategy in the Age of Artificial Intelligence”,. Journal of Strategic Studies, 391:5–6, 793–819. < 10.1080/01402390.2015.1088838 [Last accessed: 04.08.2022].
     https://doi.org/10.1080/01402390.2015.1088838 [Google Scholar]
  2. Baker, Mona
    1993 “Corpus linguistics and translation studies: Implications and applications”. InM. Baker, G. Francis, & E. Tognini-Bonelli (eds.), Text and technology: In honour of John Sinclair, 233–252. Amsterdam: John Benjamins. 10.1075/z.64.15bak
     https://doi.org/10.1075/z.64.15bak [Google Scholar]
  3. 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 Studies81, 123–157. Antwerp: Artesis University College.
     [Google Scholar]
  4. 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 Linguistics108.11, 109–120. < 10.1515/pralin‑2017‑0013 [Last accessed: 04.08.2022].
     https://doi.org/10.1515/pralin-2017-0013 [Google Scholar]
  5. 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 hdl.handle.net/1854/LU-8516838 [Last accessed: 04.08.2022].
     [Google Scholar]
  6. Edunov, Sergey; Myle, Ott; Ranzato, Marc’Aurelio & Auli, Michael
    2019 “On The Evaluation of Machine Translation Systems Trained With Back-Translation”. Retrieved fromarXiv preprint, Cornell University. https://arxiv.org/pdf/1908.05204.pdf [Last accessed: 15.04.2022].
     [Google Scholar]
  7. Fiederer, Rebecca & O’Brien, Sharon
    2009 “Quality and machine translation: A realistic objective”. The journal of Specialised translation11.111, 52–74. https://jostrans.org/issue11/art_fiederer_obrien.pdf [Last accessed: 04.08.2022].
     [Google Scholar]
  8. Forcada, Mikel L.
    2017 “Making sense of neural machine translation”. Translation spaces6.21, 291–309. < 10.1075/ts.6.2.06for [Last accessed: 04.08.2022].
     https://doi.org/10.1075/ts.6.2.06for [Google Scholar]
  9. Frederking, Robert; Mariani, Joseph & Zampolli, Antonio
    1999 Multilingual Information Management: Current Levels and Future Abilities. www.cs.cmu.edu/~ref/mlim/index.html [Last accessed: 04.08.2022].
     [Google Scholar]
  10. 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 fromarXiv preprint, Cornell University. https://arxiv.org/pdf/1904.04790.pdf [Last accessed: 04.08.2022]. 10.18653/v1/W19‑5204
     https://doi.org/10.18653/v1/W19-5204 [Google Scholar]
  11. 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. https://nettt-conference.com/wp-content/uploads/2022/07/Book-of-Abstracts-nettt.pdf [Last accessed: 04.08.2022].
     [Google Scholar]
  12. 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. https://nettt-conference.com/wp-content/uploads/2022/07/Book-of-Abstracts-nettt.pdf [Last accessed: 04.08.2022].
     [Google Scholar]
  13. Junczys-Dowmunt, Marcin
    2019 “Microsoft translator at WMT 2019: Towards large-scale document-level neural machine translation”. Retrieved fromarXiv preprint, Cornell University. [Last accessed: 15.04.2022]. 10.18653/v1/W19‑5321
     https://doi.org/10.18653/v1/W19-5321 [Google Scholar]
  14. Kersting, K., Meyer, U.
    2018 “From Big Data to Big Artificial Intelligence?”. Künstl Intell321, 3–8. < 10.1007/s13218‑017‑0523‑7 [Last accessed: 04.08.2022].
     https://doi.org/10.1007/s13218-017-0523-7 [Google Scholar]
  15. Large, Duncan
    2018 “Could Google Translate Shakespeare?”. InOther Words521, 79–98. https://ueaeprints.uea.ac.uk/id/eprint/68373/ [Last accessed: 04.08.2022].
     [Google Scholar]
  16. Moorkens, Joss,
    2018 “Translators’ perceptions of literary post-editing using statistical and neural machine translation”. Translation Spaces7.21, 240–262. < 10.1075/ts.18014.moo [Last accessed: 04.08.2022].
     https://doi.org/10.1075/ts.18014.moo [Google Scholar]
  17. 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. https://nettt-conference.com/wp-content/uploads/2022/07/Book-of-Abstracts-nettt.pdf [Last accessed: 04.08.2022].
     [Google Scholar]
  18. 2022b “How to deal with errors in machine translation: Postediting”. InDorothy Kenny (ed.), Machine translation for everyone: Empowering users in the age of artificial intelligence, 105–120. Berlin: Language Science Press. < 10.5281/zenodo.6759982 [Last accessed: 04.08.2022].
     https://doi.org/10.5281/zenodo.6759982 [Google Scholar]
  19. Rossi, Caroline & Carré, Alice
    2022 “How to choose a suitable neural machine translation solution: Evaluation of MT quality”. InDorothy Kenny (ed.), Machine translation for everyone: Empowering users in the age of artificial intelligence, 51–79. Berlin: Language Science Press. 10.5281/zenodo.6759978 [Last accessed: 04.08.2022].
     https://doi.org/10.5281/zenodo.6759978 [Google Scholar]
  20. Toral, Antonio
    2019 “Post-editese: an Exacerbated Translationese”. Retrieved formarXiv preprint, Cornell University. 10.48550/arXiv:1907.00900 [Last accessed: 15.04.2022].
     https://doi.org/10.48550/arXiv:1907.00900 [Google Scholar]
  21. Unesco, Ich
    2020 “Basic texts of the 2003 convention for the safeguarding of the intangible cultural heritage”. https://ich.unesco.org/en/basic-texts-00503 [Last accessed: 15.04.2022].
  22. 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. https://aclanthology.org/W19-6701.pdf [Last accessed: 04.08.2022].
     [Google Scholar]
/content/journals/10.1075/forum.00025.san
Loading
  • Article Type: Research Article
Keyword(s): ethics; language as Intangible Cultural Heritage; langue comme patrimoine culturel immatériel; neural machine translation; NMT; NMT reception; post-editese; réception de la NMT; traduction automatique neuronale; translationese; « post-editese »; « translationese »; éthique

Most Cited

This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error