@inproceedings{deilen-etal-2024-evaluation,
title = "Evaluation of intralingual machine translation for health communication",
author = {Deilen, Silvana and
Lapshinova-Koltunski, Ekaterina and
Garrido, Sergio and
H{\"o}rner, Julian and
Maa{\ss}, Christiane and
Theel, Vanessa and
Ziemer, Sophie},
editor = "Scarton, Carolina and
Prescott, Charlotte and
Bayliss, Chris and
Oakley, Chris and
Wright, Joanna and
Wrigley, Stuart and
Song, Xingyi and
Gow-Smith, Edward and
Bawden, Rachel and
S{\'a}nchez-Cartagena, V{\'\i}ctor M and
Cadwell, Patrick and
Lapshinova-Koltunski, Ekaterina and
Cabarr{\~a}o, Vera and
Chatzitheodorou, Konstantinos and
Nurminen, Mary and
Kanojia, Diptesh and
Moniz, Helena",
booktitle = "Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1)",
month = jun,
year = "2024",
address = "Sheffield, UK",
publisher = "European Association for Machine Translation (EAMT)",
url = "https://aclanthology.org/2024.eamt-1.39",
pages = "469--479",
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 outputs from three models based on different criteria, such as correctness, readability, and syntactic complexity. We compare the outputs of the three models under analysis between each other, as well as with the existing human translations. The study revealed that system performance depends on the evaluation criteria used and that only one of the three models showed strong similarities to the human translations. Furthermore, we identified various types of errors in all three models. These included not only grammatical mistakes and misspellings, but also incorrect explanations of technical terms and false statements, which in turn led to serious content-related mistakes.",
}
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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 outputs from three models based on different criteria, such as correctness, readability, and syntactic complexity. We compare the outputs of the three models under analysis between each other, as well as with the existing human translations. The study revealed that system performance depends on the evaluation criteria used and that only one of the three models showed strong similarities to the human translations. Furthermore, we identified various types of errors in all three models. These included not only grammatical mistakes and misspellings, but also incorrect explanations of technical terms and false statements, which in turn led to serious content-related mistakes.</abstract>
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%0 Conference Proceedings
%T Evaluation of intralingual machine translation for health communication
%A Deilen, Silvana
%A Lapshinova-Koltunski, Ekaterina
%A Garrido, Sergio
%A Hörner, Julian
%A Maaß, Christiane
%A Theel, Vanessa
%A Ziemer, Sophie
%Y Scarton, Carolina
%Y Prescott, Charlotte
%Y Bayliss, Chris
%Y Oakley, Chris
%Y Wright, Joanna
%Y Wrigley, Stuart
%Y Song, Xingyi
%Y Gow-Smith, Edward
%Y Bawden, Rachel
%Y Sánchez-Cartagena, Víctor M.
%Y Cadwell, Patrick
%Y Lapshinova-Koltunski, Ekaterina
%Y Cabarrão, Vera
%Y Chatzitheodorou, Konstantinos
%Y Nurminen, Mary
%Y Kanojia, Diptesh
%Y Moniz, Helena
%S Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1)
%D 2024
%8 June
%I European Association for Machine Translation (EAMT)
%C Sheffield, UK
%F deilen-etal-2024-evaluation
%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 outputs from three models based on different criteria, such as correctness, readability, and syntactic complexity. We compare the outputs of the three models under analysis between each other, as well as with the existing human translations. The study revealed that system performance depends on the evaluation criteria used and that only one of the three models showed strong similarities to the human translations. Furthermore, we identified various types of errors in all three models. These included not only grammatical mistakes and misspellings, but also incorrect explanations of technical terms and false statements, which in turn led to serious content-related mistakes.
%U https://aclanthology.org/2024.eamt-1.39
%P 469-479
Markdown (Informal)
[Evaluation of intralingual machine translation for health communication](https://aclanthology.org/2024.eamt-1.39) (Deilen et al., EAMT 2024)
ACL
- Silvana Deilen, Ekaterina Lapshinova-Koltunski, Sergio Garrido, Julian Hörner, Christiane Maaß, Vanessa Theel, and Sophie Ziemer. 2024. Evaluation of intralingual machine translation for health communication. In Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1), pages 469–479, Sheffield, UK. European Association for Machine Translation (EAMT).