TRANSLATING THAT: AN IDEATIONAL CORRESPONDENCE ANALYSIS OF MACHINE TRANSLATION

Abstract

This paper aims to contrast the translation of two machine translation systems, Google Translate and Bing Translator, in translating the lexeme in news articles. The approach used in scrutinizing the lexeme's translation correspondence in this study is systemic functional linguistics, especially in both experiential and logical structures. This study was carried out through descriptive comparative analysis. This study's data were 40 constituents that were taken from six BBC World news articles randomly selected. A thorough analysis demonstrates that the two machine translation systems can recognize the three functions of that, i.e., Head, post-modifier, and conjunction. The highest emerging function is post-modifier by 19 times (47.5%), followed by the conjunction function by 17 times (42.5%) on the first machine translation system and 18 times (45%) on the second one. The lowest emerging function is Head by four times (10%) on the first machine translation system and three times (7.5%) on the second one. Furthermore, due to the elliptical variation of that as a relative pronoun and the translation variation of that as a post-determiner, it concludes that the translation outputs of Google Translate are more accurate, semantically acceptable, creative, and contextual than those of Bing Translator.