@inproceedings{maass-fioravanti-2025-translating,
    title = "Translating Easy Language administrative texts: a quantitative analysis of {D}eep{L}{'}s performance from {G}erman into {I}talian using a bilingual corpus",
    author = "Maa{\ss}, Christiane  and
      Fioravanti, Chiara",
    editor = "Ginel, Mar{\'i}a Isabel Rivas  and
      Cadwell, Patrick  and
      Canavese, Paolo  and
      Hansen-Schirra, Silvia  and
      Kappus, Martin  and
      Matamala, Anna  and
      Noonan, Will",
    booktitle = "Proceedings of the 1st Workshop on Artificial Intelligence and Easy and Plain Language in Institutional Contexts (AI {\&} EL/PL)",
    month = jun,
    year = "2025",
    address = "Geneva, Switzerland",
    publisher = "European Association for Machine Translation",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.aielpl-1.7/",
    pages = "66--72",
    ISBN = "978-2-9701897-5-6",
    abstract = "This study evaluates the performance of DeepL as an AI-based translation engine, in translating German Easy Language Texts into Italian. The evaluation is 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."
}