Predicting Errors in Google Translations of Online Health Information

Abstract

Machine translation technology is having increasing applications in health and medical settings that involve communications and interactions between people from diverse language, cultural background. Machine translation tools offer low-cost, and accessible solutions to help close the gap in cross-lingual health communications. The risks of machine translation need to be effectively controlled and properly managed to boost the confidence in this developing technology among health professionals. This study integrates the methodological benefits of machine learning in machine translation quality evaluation, and more importantly, the prediction of clinically relevant machine translation errors based on the study of linguistic features of the English source texts.