TRANSLATION ERROR IN GOOGLE TRANSLATE FROM ENGLISH INTO ROMANIAN IN TEXTS RELATED TO CORONAVIRUS

Abstract

Both the emergence of the pandemic and lack of knowledge and/or time needed to translate texts related to this topic brought about an increased interest in analyzing Neural Machine Translation (NMT) performance. This study aims to identify and analyze lexical and semantic errors of language aspects that appear in medical texts translated by Google Translate from English into Romanian. The data used for investigation comprises official prospects of 5 vaccines that were approved to be used against the current coronavirus. The focus is on the lexical and semantic errors, as researchers state that these errors made by Machine Translation have the highest frequency compared to morphological or syntactic errors. Moreover, the lexical errors may affect the meaning, the message, and may easily lead to mistranslation, misunderstanding and, therefore, misinformation. The texts to be analyzed are collected from official websites and translated using Google Translate and Google Languages Tools. From the data analyzed, there are 22 lexical and semantic errors that are approached through descriptive methodology. By examining types of errors in translation from English into Romanian and analyzing the potential causes of errors, the results will be used to illustrate the quality and accuracy of Google Translate when translating public health information from English into Romanian, to observe how much the message is affected by the error, in order to sharpen up linguistic awareness. The results of the study can ultimately help improve of the quality of NMT in terms of better lexical selection and attempt to give inputs as a contribution for a more adequate translation into Romanian by Google Machine Translation.