Inverse Translation Quality: A Comparative Analysis between Human Translation and Post-editing

Abstract

This paper aims to analyse the quality of inverse translation and to see whether or not trainee translators, such as undergraduate language students, can produce translations between foreign languages, and whether or not post-editing machine translation and translation memories, have any effect on the Malay students’ performance. Through error analysis approach, this paper also aims to reveal the contributing factors to the mistakes the students did in their translations and uncover the nature of Google Translate by identifying the recurring types of errors in the MT outputs. Results revealed that the translation technologies, particularly in the post-editing modified translation memories and machine translation tasks, helped the students improved the quality of their translations, suggesting that non-native speakers can be highly skilled professional translators with years of experience and proper training. Based on the error analysis, syntactic and lexical errors seem to be problematic in Google Translate in both Arabic-English and English-Arabic translations, implying that proper guidelines are crucial in post-editing so that post-editors can be aware of the potential recurrent errors and not overlook them. Also, the study identified that linguistic interference might have significantly influenced the students’ performance as the three languages differ from one another in many aspects.