TOWARDS BUILDING A TAXONOMY OF MACHINE TRANSLATION ERRORS: VERSATILITY VS TARGETED APPROACHES

This study examines problems in existing approaches to classifying machine translation (MT) errors. Despite the fact that such classifications can be based on a taxonomy developed on the material of traditional translations, MT errors have their own specifics, which should also be considered in the classification system. The first part of the presented paper discusses the concepts of machine translation and translation error per se and provides an overview of the main approaches to building a taxonomy of translation errors in traditional, “human” translation. In the second part, we discuss the existing classification systems proposed for the analysis of machine translation output and some of their limitations. The research material in the discussed papers is mainly focused on translations in pairs with the Russian language and obtained through the use of the most popular in Russia machine translation services, viz. Yandex Translator, Google Translate and Promt. In particular, we discuss the main classes of errors identified by various authors, the frequency of the errors of the said classes, as well as their gravity in terms of their ability to cause a communication failure. This study argues that there is currently no unified approach to the construction of such classifications, as well as that they are inevitably dependent on the type of the analysed text, the language pair and the chosen automated translation system. It is concluded that the optimal approach to classifying MT errors is not a universal approach, but a targeted one, that is, depending on certain translation parameters. The possibility of practical applications of machine translation in teaching foreign languages and in the work of professional translators, as well as the need for post-editing of translated texts, are also discussed.