Ecotranslatological capabilities of DeepL Translator at the lexical and grammatical level (French – Ukrainian language pair)

Abstract

The article deals with the ecotranslatology of the specialized context using the DeepL neural machine service at the lexical and grammatical level from French into Ukrainian. The continuous increase in the scientific and technical information coming in European languages makes it necessary to urgently acquaint the domestic reader with the latest achievements in the intellectual progress. The goal of the article is an ecotranslatological analysis of the neural machine translator’s capability to adequately reflect a French message in Ukrainian in accordance with its lexical and grammatical norms. The article focuses on the linguistic ecology of the original context to be maintained and transferred by the translator, i.e. the capabilities of the neural machine translator of selecting and adapting lexical and grammatical elements using the translation memory available in the data basis. The quality criterion to be used to assess the adequacy of the translated text is the ecotranslatological principle of maintaining the ecobalance between the two languages involved in the translation process – it is achieved by applying replication, reduction and addition. A comparative analysis has shown the “ability” of DeepL Translator to perform adaptive omissions, additions, word order changes, antonymic translations, contextual specifications, conversions and morphological replacements. Nowhere have we noticed any grammatical interference or lexical literalism. As a result, our research has not revealed any knowledge gaps of DeepL Translator in the lexical and grammatical equivalence, which supports other scientists’ statements that DeepL Translator is nowadays the world’s best tool able to provide a fast and adequate translation of any scientific and technical text without calling for much post-editing work.