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

Autor: O'Brien, Sharon, Rossetti, Alessandra
Předmět:
Zdroj: Journal of Internationalization & Localization; 2020, Vol. 7 Issue 1/2, p95-121, 27p
Abstrakt: 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, and 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. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index