A survey of machine translation competences: Insights for translation technology educators and practitioners

Abstract

This paper describes a large-scale survey of machine translation (MT) competencies conducted by a non-commercial and publicly funded European research project. Firstly, we highlight the increased prevalence of translation technologies in the translation and localisation industry, and develop upon this by reporting on survey data derived from 438 validated respondents, including freelance translators, language service providers, translator trainers, and academics. We then focus on ascertaining the prevalence of translation technology usage on a fine-grained scale to address aspects of MT, quality assessment techniques and post-editing. We report a strong need for an improvement in quality assessment methods, tools, and training, partly due to the large variance in approaches and combinations of methods, and to the lack of knowledge and resources. We note the growing uptake of MT and the perceived increase of its prevalence in future workflows. We find that this adoption of MT has led to significant changes in the human translation process, in which post-editing appears to be exclusively used for high-quality content publication. Lastly, we echo the needs of the translation industry and community in an attempt to provide a more comprehensive snapshot to inform the provision of translation training and the need for increased technical competencies.