Quality and productivity: A comparative analysis of human translation and post-editing with Malay learners of Arabic and English

Translation into and between foreign languages has become a common practice in the professional setting. However, this translation directionality has yet to be thoroughly explored, especially when post-editing is involved. The present study conducts experiments on the application of machine translation (MT) and translation memory (TM) in a translation classroom setting. A group of Malay speakers, who are non-native speakers of Arabic and English, used MemoQ 2014 to translate technical Arabic and English texts by post-editing raw MT and modified TM outputs containing several errors. The non-native trainee translators’ productivity was measured and the quality of the translation was assessed through error analysis approach based on the MeLLANGE error typology so that it could provide a comprehensive analysis of the types of errors commonly found in the non-native trainee translators’ translations. The error annotation also aims to provide guidelines for translators who work with the Arabic-English language pair and non-native translators.
The present study revealed that the translation technologies helped improve the non-native translators’ speed and quality. The study also discovered that syntactic and lexical errors are the most problematic in the PE tasks. The trainee translators tend to overlook the errors that were caused by cross-linguistic influence, such as articles, gender, number and the conjunction “wa”. However, this could have been avoided if the participants revised their translations thoroughly because most of the errors are minor. The study also revealed that the non-native trainee translators could be as productive as the professional native translators because they managed to reach the average daily productivity for professional translators, which is at least 5,000 words per day.