@inproceedings{deilen-etal-2024-towards,
    title = "Towards {AI}-supported Health Communication in Plain Language: Evaluating Intralingual Machine Translation of Medical Texts",
    author = {Deilen, Silvana  and
      Lapshinova-Koltunski, Ekaterina  and
      Hern{\'a}ndez Garrido, Sergio  and
      Maa{\ss}, Christiane  and
      H{\"o}rner, Julian  and
      Theel, Vanessa  and
      Ziemer, Sophie},
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Thompson, Paul  and
      Ondov, Brian",
    booktitle = "Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.cl4health-1.6",
    pages = "44--53",
    abstract = "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.",
}

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.
Deilen, Silvana Lapshinova-Koltunski, Ekaterina Hern{\'a}ndez Garrido, Sergio Maa{\ss}, Christiane H{\"o}rner, Julian Theel, Vanessa Ziemer, Sophie
Towards AI-supported Health Communication in Plain Language: Evaluating Intralingual Machine Translation of Medical Texts