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Neural machine translation and the evolution of the localisation sector: implications for training

O'Brien, Sharon orcid logoORCID: 0000-0003-4864-5986 and Rossetti, Alessandra orcid logoORCID: 0000-0002-2162-9639 (2020) Neural machine translation and the evolution of the localisation sector: implications for training. Journal of Internationalization and Localization, 7 (1/2). pp. 95-121. ISSN 2032-6904

Abstract
The localisation sector is highly technologized and evolves rapidly. Though significant consideration has been given to third-level training in localisation for Translation Studies students, the nature of the industry is such that this topic demands regular attention. Our objective was to survey employees and executive managers to understand what impact recent technological developments, including but not limited to neural machine translation (NMT), might have on future skills and training requirements for localisation linguists. Our findings are that linguists in localisation take up a variety of roles, including transcreation, data mining, project and vendor management. NMT is considered an important advancement, and its introduction has emphasised the need for a critical use of technology, while opening new career pathways, such as data curation and annotation. Domain specialisation is recommended for those who translate, and transferable soft skills are more essential than ever. Increased industry and interdisciplinary collaborations in training are also considered valuable.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Localisation; training; employability; translation graduate skills
Subjects:Computer Science > Machine translating
Humanities > Translating and interpreting
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Humanities and Social Science > School of Applied Language and Intercultural Studies
Research Institutes and Centres > Centre for Translation and Textual Studies (CTTS)
Research Institutes and Centres > ADAPT
Publisher:John Benjamins
Official URL:https://benjamins.com/catalog/jial.20005.obr
Copyright Information:© John Benjamins Publishing Company
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland through the SFI Research Centres Programme, cofunded under the European Regional Development Fund through Grant no. 13/RC/2106
ID Code:25321
Deposited On:06 Jan 2021 11:44 by Sharon O'brien . Last Modified 06 Jan 2021 11:44
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