The tourism sector and tourism discourse are strongly influenced by English, not only because English is the international language, but also because tourism texts are increasingly produced and disseminated in digital environments rather than solely in print. The development of artificial intelligence and machine translation tools has further transformed the writing and translation of tourism texts. In my master’s thesis, I decided to research how the DeepL translator renders English linguistic elements in tourism texts into French and Slovenian. The analysis is based on three principal theoretical frameworks: Newmark’s theory of communicative and semantic translation, Nida’s theory of functional and formal equivalence, and Venuti’s theory of domestication and foreignization, which serve to identify the predominant translation strategies in both target languages.
My corpus consists of five sections, each comprising three tourism texts from a specific tourism domain (culture, nature, gastronomy, the hotel industry, and travel agency programmes). My comparative analysis revealed significant differences in translation strategies between French and Slovenian. More specifically, when translating into French, DeepL employs domestication strategies more frequently, demonstrating greater success in identifying appropriate native equivalents and producing communicative translations. As a result, French translations are generally more fluent and more likely to achieve functional equivalence. Slovenian translations, by contrast, retain the influence of the English source text nearly twice as often and more frequently preserve untranslated English terms, indicating a weaker tendency toward terminological standardization. My analysis also shows that Slovenian translations generated by DeepL contain a higher number of translation and spelling errors, whereas French translations require less human intervention.
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