Translation Shifts on Reference by Machine Translation in Descriptive Text

Abstract

Translation shifts are one of strategy to get a high-quality translation. It’s also used to solve the absent meaning on the target text. The objectives of this research are to describe the translation shifts (based on the theory of Blum-Kulka about kinds of shift and Halliday and Matthiesen on cohesion theory), which are done by machine translation in descriptive texts. The researcher used a descriptive qualitative research design to achieve the aims of this research. The source of data in this research is descriptive text. The data of this research are pair of words in source and target text. The form of words (pair of words in source and target) are in reference form based on the theory of Halliday about lexical devices. The researcher used interactive data analysis (data condensation, data display, and verifying/conclusion) to get the research findings. This research shows that Yandex translation made translation shifts more (35 times) often than the others. From the whole types of translation shifts (cohesion shifts: implicitation, explicitation, and meaning change), implicitation shift placed a high frequency among machines translation, however explicitation shift placed in the low frequency, and the medium frequency is placed by meaning change. It is to indicate that machine translation still lacks to produce a high level in the target than a source.