@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Towards AI-supported Health Communication in Plain Language: Evaluating Intralingual Machine Translation of Medical Texts
%A Deilen, Silvana
%A Lapshinova-Koltunski, Ekaterina
%A Hernández Garrido, Sergio
%A Maaß, Christiane
%A Hörner, Julian
%A Theel, Vanessa
%A Ziemer, Sophie
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Thompson, Paul
%Y Ondov, Brian
%S Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F deilen-etal-2024-towards
%X 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.
%U https://aclanthology.org/2024.cl4health-1.6
%P 44-53
Markdown (Informal)
[Towards AI-supported Health Communication in Plain Language: Evaluating Intralingual Machine Translation of Medical Texts](https://aclanthology.org/2024.cl4health-1.6) (Deilen et al., CL4Health-WS 2024)
ACL