Beyond the margins of academic education: Identifying translation industry training practices through action research

Digital technologies in the translation profession have given rise to the use of automated Computer Assisted Translation (CAT) tools and Machine Translation (MT), and Translation Service Providers are embracing these innovations as part of their workflows. Higher Education Institutions are also tran...

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Bibliographic Details
Published in:The international journal of translation and interpreting research 2020-01, Vol.12 (1), p.115-126
Main Author: de Cespedes, Begona Rodriguez
Format: Article
Language:English
Subjects:
Action research
action research
translator training
automation
future proofing
Artificial intelligence
Automation
Curriculum
Education
Educational facilities
Employers
Higher education
Machine translating
Machine translation
Managers
Methodology
Professions
Spain
Technology
Training
Training of
Translation instruction
Translation methods and strategies
Translations
Translators
Translators (Persons)
United Kingdom
Workers
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Summary:Digital technologies in the translation profession have given rise to the use of automated Computer Assisted Translation (CAT) tools and Machine Translation (MT), and Translation Service Providers are embracing these innovations as part of their workflows. Higher Education Institutions are also transforming their curricula to adapt to the changes brought about by technology (Austermuhl, 2006, 2013; Doherty, Kenny, and Way 2012; Doherty and Moorkens, 2013; Gaspari, Almaghout, and Doherty, 2015; Mellinger, 2017; Moorkens, 2017; O'Hagan, 2013; Rothwell and Svoboda, 2017). This research takes a phenomenological and ethnographical approach using action research as the methodology to see how the new digital skillsets are taught and used in the translation industry. As a trainer -researcher, I stay at translation companies to immerse myself in the training given to new employees. The results of this qualitative-type research derive from observations typically involving the trainer spending a full working week at the employers' premises. The data set is hence collected based on workplace observations within the companies and on semi-structured interviews with translation company managers. This approach permits a very full understanding of the skills needed in the translation profession. What has been learned in the workplace can be applied at university in the training of future translators. Preliminary work suggests that MT and Artificial Intelligence (AI), while transforming the profession in many ways, are not yet overriding the need of sophisticated linguistic skills from trainee translators.
ISSN:1836-9324
1836-9324
DOI:10.12807/ti.112201.2020.a07