Productivity of machine translation in translating technical documents: an analysis of Google Translate output

Abstract

Translation studies is a significant area in Applied Linguistics that has expanded its scope during the past years. In the early days, the translation process was performed manually as a human process, but technological advancement paved the way for upgrading the translation process easier and faster. As a result, Machine Translation, the method of translating a text using a Computer System gained vast popularity amidst some issues in the quality of the translation. Therefore, this study aims to examine the productivity of Machine Translation in translating six types of Technical Documents: a newspaper article, a notice, a job interview advertisement, an academic article, a circular, and a press release using Google Translate Output. Giving priority to the sentence pattern and the morphological difference in both language and the translation, the source texts were analysed. All these texts are in English and expect to be translated into Sinhalese using Google Translate. Apart from that, the present study plans to check the role of the post-editing process of technical documents. The results are analysed using the Mixed Research Method. As findings of this study, it was identified that Google Translate could identify most of the technical terms in the notice and the job advertisement out of all the documents. In much longer phrases in the newspaper article and the academic article, the content of the translation became complex to convey the exact meaning as there were few grammatical errors. In some instances, it was not productive enough to get a clear and faithful translation. However, these findings paved the way to conclude that, when comparing the productivity of these documents mentioned above, Google Translate is more applicable in more informative texts such as the notice and the advertisement in Technical Translation. However, post- editing process is required to deliver an accurate and high-quality output faithful to source text when translating technical documents.