• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.199-204
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• 2022

This study was carried out in an L2 writing class using machine translation. The aim of this study was to explore what types of errors are identified the most frequently in the Korean-to-English machine translation output and what causes those errors. The participants were seven EFL university students who completed three writing tasks throughout the semester. The findings of data analysis indicated that the most common errors were seen in sentence structure and mechanics, and those errors in the translated texts were caused by the errors in the Korean source texts.