EVALUATING THE QUALITY OF TRANSLATIONS PRODUCED BY MACHINES AS OPPOSED TO HUMAN - Ahmez Abdul Jabbar; Evert H. Hilman (PROJECT (Professional Journal of English Education), 2024)
This study aims to determine the degree to which machine translations, specifically Google Translate, DeepL, and Microsoft Bing Translator, in terms of accuracy, acceptability, and readability, focusing on the translation of parts of lexical elements, namely collocations, idioms, and fixed expressions in the novel “Animal Farm” by George Orwell. In order to analyze the data, a descriptive-qualitative method with purposive sampling technique was utilized. The main theories used are Translation Methods and Procedures by Newmark and Translation Shifts by Catford as the supporting theory. To achieve greater accuracy in the results, the linguistic structure is also thoroughly examined in this research to ascertain the equivalency of the source and target languages. Out of a number of phrases that were examined and sifted on the basis of lexical element categories, (37) collocations, (49) idioms, and (24) fixed expressions were identified along with their corresponding translation quality parameters and scores. The data analysis revealed that the most accurate translation method was human translation (TL), with a weighted average score of 2.74. Google Translate (GT), Microsoft Bing Translator (MT) (with a score of 2.66), and DeepL (DL) (with a score of 2.33).