A Study on Automatic Machine Translation Tools: A Comparative Error Analysis Between DeepL and Yandex for Russian-Italian Medical Translation

Abstract

The present research is aimed at conducting a study with regard to Russian-Italian medical translation on the current development of two Machine Translation tools that feature prominently in today’s Neural Machine Translation framework, namely DeepL and Yandex. For the purpose of our research, we have selected a number of Russian medical articles: three highly specialized and three popular-science articles concerning coronavirus pandemic. Such a choice is justified by the willingness not only to analyse recently published scientific documents but also to investigate the particular linguistic implications of 2020’s coronavirus pandemic outbreak. In fact, during the period of pandemic a set of terms has been introduced and coined in  every-day communication and entered the boundaries of scientific terminology. We have considered this existing linguistic phenomenon as a proper condition to test the performances of Machine Translation tools. In particular, we discuss the most relevant features of the comparative error analysis as well as the BLEU metric for both DeepL and Yandex.