Translating Easy Language administrative texts: a quantitative analysis of DeepL’s performance from German into Italian using a bilingual corpus

This study evaluates the performance of DeepL as an AI-based translation engine, in translating German Easy Language Texts into Italian. The evaluation is quantitative and based on a corpus of 26 German fact sheets and their Italian human translations. The results show that DeepL's translations exhibit significant errors in terminology, accuracy, and language conventions. The machine-translated texts often lack consistency in terminology, and the use of technical or unfamiliar words is not adapted to the difficulty level of the target language. Furthermore, the translations tend to normalize the texts towards standard administrative language, making them less accessible. The study highlights the need for human post-editing to ensure both accuracy and suitability of the translated texts. The findings of this study will help identify where to prioritize post-editing efforts and facilitate comparisons with the results obtained from other artificial intelligence tools used for interlingual translation of Easy Language texts in the administrative domain.