DeepL Translate and DeepL Write as Tools for Text Mediation in Plurilingual Workplaces

• Authors: Konrad Klimkowski

Abstracts

EN

This article discusses how machine translation software can be used in developing users’ language and communicative skills in the workplace. The case in point is the neural machine engine DeepL, whose primary functionality is text translation; yet when one thinks about modern workplaces, it turns out that professionals other than translators may be in need of urgent text creation in a foreign language. Knowledge of the target language is an indisputable prerequisite for effective specialist communication. Nonetheless, with the use of a machine translator like DeepL Translator or machine text composer like DeepL Write creating texts in the target language (formal emails, minutes or summaries) may take less time and give a more satisfactory effect both in terms of text quality and authors’ foreign language practice.

Keywords

EN

NMT; DeepL Translator; DeepL Write; translation; postediting; plurilingual workplaces

• Publisher: Komisja Nauk Filologicznych Polskiej Akademii Nauk, Oddział we Wrocławiu
• Journal: Academic Journal of Modern Philology
• Year: 2023
• Issue: 19
• Pages: 163-175

Dates

published

2023

Contributors

author

Konrad Klimkowski

• Maria Sklodowska-Curie University (UMCS), Department of Applied Linguistics

References

• Briggs, Neil (2018) “Neural Machine Translation Tools in the Language Learning Classroom: Students’ Use, Perceptions, and Analyses.” [In:] The JALT CALL Journal, vol. 14/1; 3–24.
• Carré, Alice, Dorothy Kenny, Caroline Rossi, Pilar Sánchez-Gijón, Olga Torres-Hostench (2022) “Machine translation for language learners.” Translation and Multilingual Natural Language Processing, vol. 18. [At:] https://doi.org/10.5281/ZENODO.6760024 [date of access: 20 Apr. 2023]; 187–207.
• Castilho, Sheila (2022) “How Much Context Span is Enough? Examining Context-Related Issues for Document-level MT.” [In:] Proceedings of the Thirteenth Language Resources and Evaluation Conference. [At:] https://aclanthology.org/2022.lrec-1.323 [date of access: 20 Apr. 2023]; 3017–3025.
• Castilho, Sheila, Clodagh Mallon, Rahel Meister, Shengya Yue (in print). “Do Online Machine Translation Systems Care for Context? What About a GPT Model?” Retrieved from https://doras.dcu.ie/28297/ on 20 Apr. 2023.
• Coste, Daniel, Danièle Moore, Geneviève Zarate (2009) Plurilingual and Pluricultural Competence. Council of Europe Publishing.
• Council of Europe (2020) Common European Framework of Reference for Languages: Learning, Teaching, Assessment: Companion Volume. Strasbourg: Council of Europe Publishing.
• Goodwin-Jones, Robert (2022) “Partnering with AI: Intelligent Writing Assistance and Instructed Language Learning.” [In:] Language Learning and Technology, vol. 26/2; 5–24.
• Jolley, Jason R., Luciane Maimone (2022) “Thirty Years of Machine Translation in Language Teaching and Learning: A Review of the Literature.” [In:] L2 Journal, vol. 14/1. [At:] https://doi.org/10.5070/L214151760 [date of access: 20 Apr. 2023]; 26–44.
• Klimkowski, Konrad (2015) Towards a Shared Curriculum in Translator and Interpreter Education. Wrocław: WSF, Wrocław; Institute of Communicology.
• Läubli, Samuel, Rico Sennrich, Martin Volk (2018) “Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation.” [In:] Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. [At:] https://doi.org/10.18653/v1/D18-1512 [date of access: 20 Apr. 2023]; 4791–4796.
• Lee, Michelle A. (2021) Machine Translation Systems and Translation Quality using the Back Translation Method [Graduate Degree Thesis]. University of Wisconsin-Stout. Retrieved from https://minds.wisconsin.edu/bitstream/handle/1793/83072/2021leem.pdf on 20 Apr. 2023.
• Niño, Ana (2020) “Exploring the use of online machine translation for independent language learning.” [In:] Research in Learning Technology, vol. 28; n.d. Retrieved from https://doi.org/10.25304/rlt.v28.2402 on 20 Apr. 2023.
• Piccardo, Enrica, Aline Germain-Rutherford, Geoff Lawrence (2021) The Routledge Handbook of Plurilingual Language Education. New York: Routledge.
• Poibeau, Thierry (2017) Machine Translation. Cambridge, Massachusetts: The MIT Press.
• Stahlberg, Felix (2020) "Neural Machine Translation: A Review." [In:] Journal of Artificial Intelligence Research 69. [At:] doi: 10.1613/jair.1.12007 [date of access: 20 Apr. 2023]; 343–418.
• Toral, Antonio, Sheila Castilho, Ke Hu, Andy Way (2018) “Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation.” [In:] Proceedings of the Third Conference on Machine Translation: Research Papers. [At:] https://doi.org/10.18653/v1/W18-6312 [date of access: 20 Apr. 2023]; 113–123.
• Voita, Elena, Rico Sennrich, Ivan Titov (2019) “When a Good Translation is Wrong in Context: Context-Aware Machine Translation Improves on Deixis, Ellipsis, and Lexical Cohesion.” [In:] Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. [At:] https://doi.org/10.18653/v1/P19-1116 [date of access: 20 Apr. 2023]; 1198–1212.
• DeepL: https://www.deepl.com/ [date of access: 20 April 2023].
• Meta: https://about.meta.com/ [date of access: 20 April 2023].
• Workplace: https://www.workplace.com/ and https://www.workplace.com/features/auto-translate [date of access: 20 April 2023].
• Omniscien Technologies: https://omniscien.com/ and https://omniscien.com/faq/who-uses-machine-translation/ [date of access: 20 April 2023].
• United Language Group: https://www.unitedlanguagegroup.com/ and https://www.unitedlanguagegroup.com/blog/how-companies-are-using-machine-translation-to-open-the-lines-of-communication [date of access: 20 April 2023].

Identifiers

DOI

10.34616/ajmp.2023.19.11

Biblioteka Nauki

27311262