How to train future translators?

What is it about?

With increasing acknowledgment of enhanced quality now achievable by Machine Translation, new possibilities have emerged through collaboration between human and machine in the translation process, including providing varying qualities of translation in response to quality/efficiency requirements. This paper presents surveys of post-graduate students of translation conducted over four consecutive years to examine if their awareness and preparedness have kept pace with these possibilities. It is found that respondents across the years generally perceive their awareness as lacking, are hesitant in employing MT, and show marked reservations when reconsidering issues such as quality and the preeminent position of the human translator. A review of existing research in translator training points towards a lopsided emphasis on linguistic competence and standalone courses for introducing technology as the primary cause behind low adoption. The need of the hour is translator training that fully integrates technology in the translation process and also provides a clear framework to adjust quality/efficiency is important to ensure preparedness. A repeat survey of students from 2021 who were trained under this model shows an increase in willingness to use MT and to consider quality as dependent on intended use. The focus here is on Chinese-English translation, but the discussion may find resonance with other language pairs. Keywords: translator training, computer-assisted translation, Machine Translation, translation pedagogy, Chinese-English translation

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Photo by Steve Johnson on Unsplash

Photo by Steve Johnson on Unsplash

Why is it important?

Translation is experiencing massive disruption with the advent of NMT and now generative AI. It is important to help future translators adapt to new realities.