In this paper, we describe results of a study on evaluation of intralingual machine translation. The study focuses on machine translations of medical texts into Plain German. The automatically simplified texts were compared with manually simplified texts (i.e., simplified by human experts) as well as with the underlying, unsimplified source texts. We analyse the quality of the translations based on different criteria, such as correctness, readability, and syntactic complexity. The study revealed that the machine translations were easier to read than the source texts, but contained a higher number of complex syntactic relations than the human translations. Furthermore, we identified various types of mistakes. These included not only grammatical mistakes but also content-related mistakes that resulted, for example, from mistranslations of grammatical structures, ambiguous words or numbers, omissions of relevant prefixes or negation, and incorrect explanations of technical terms.

This study sets out to investigate the feasibility of using ChatGPT to translate citizen-oriented administrative texts into German Easy Language, a simplified, rule-based language variety that is adapted to the needs of people with reading impairments. We use ChatGPT to translate selected texts from websites of German public authorities using two strategies, i.e. linguistic and holistic. We analyse the quality of the generated texts based on different criteria, such as correctness, readability, and syntactic complexity. The results indicated that the generated texts are easier than the standard texts, but that they still do not fully meet the established Easy Language standards. Additionally, the content is not always rendered correctly.