@inproceedings{simonsen-einarsson-2024-human,
title = "A Human Perspective on {GPT}-4 Translations: Analysing {F}aroese to {E}nglish News and Blog Text Translations",
author = "Simonsen, Annika and
Einarsson, Hafsteinn",
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-1.7",
pages = "24--36",
abstract = "This study investigates the potential of Generative Pre-trained Transformer models, specifically GPT-4, to generate machine translation resources for the low-resource language, Faroese. Given the scarcity of high-quality, human-translated data for such languages, Large Language Models{'} capabilities to produce native-sounding text offer a practical solution. This approach is particularly valuable for generating paired translation examples where one is in natural, authentic Faroese as opposed to traditional approaches that went from English to Faroese, addressing a common limitation in such approaches. By creating such a synthetic parallel dataset and evaluating it through the Multidimensional Quality Metrics framework, this research assesses the translation quality offered by GPT-4. The findings reveal GPT-4{'}s strengths in general translation tasks, while also highlighting its limitations in capturing cultural nuances.",
}
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<abstract>This study investigates the potential of Generative Pre-trained Transformer models, specifically GPT-4, to generate machine translation resources for the low-resource language, Faroese. Given the scarcity of high-quality, human-translated data for such languages, Large Language Models’ capabilities to produce native-sounding text offer a practical solution. This approach is particularly valuable for generating paired translation examples where one is in natural, authentic Faroese as opposed to traditional approaches that went from English to Faroese, addressing a common limitation in such approaches. By creating such a synthetic parallel dataset and evaluating it through the Multidimensional Quality Metrics framework, this research assesses the translation quality offered by GPT-4. The findings reveal GPT-4’s strengths in general translation tasks, while also highlighting its limitations in capturing cultural nuances.</abstract>
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%0 Conference Proceedings
%T A Human Perspective on GPT-4 Translations: Analysing Faroese to English News and Blog Text Translations
%A Simonsen, Annika
%A Einarsson, Hafsteinn
%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 simonsen-einarsson-2024-human
%X This study investigates the potential of Generative Pre-trained Transformer models, specifically GPT-4, to generate machine translation resources for the low-resource language, Faroese. Given the scarcity of high-quality, human-translated data for such languages, Large Language Models’ capabilities to produce native-sounding text offer a practical solution. This approach is particularly valuable for generating paired translation examples where one is in natural, authentic Faroese as opposed to traditional approaches that went from English to Faroese, addressing a common limitation in such approaches. By creating such a synthetic parallel dataset and evaluating it through the Multidimensional Quality Metrics framework, this research assesses the translation quality offered by GPT-4. The findings reveal GPT-4’s strengths in general translation tasks, while also highlighting its limitations in capturing cultural nuances.
%U https://aclanthology.org/2024.eamt-1.7
%P 24-36
Markdown (Informal)
[A Human Perspective on GPT-4 Translations: Analysing Faroese to English News and Blog Text Translations](https://aclanthology.org/2024.eamt-1.7) (Simonsen & Einarsson, EAMT 2024)
ACL