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    <abstract>For this shared task, we have used several machine translation engines to produce translations (en ⇔ fr) by fine-tuning a dialog-oriented NMT engine and having NMT baseline translations post-edited with prompt engineering. Our objectives are to test the effectiveness of a fine-tuning strategy with help of a robust NMT model, to draw out a from-translation-to-post-editing pipeline, and to evaluate the strong and weak points of NMT systems.</abstract>
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%0 Conference Proceedings
%T Enhancing Translation Quality: A Comparative Study of Fine-Tuning and Prompt Engineering in Dialog-Oriented Machine Translation Systems. Insights from the MULTITAN-GML Team
%A Zhu, Lichao
%A Zimina, Maria
%A Namdarzadeh, Behnoosh
%A Ballier, Nicolas
%A Yunès, Jean-Baptiste
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Ninth Conference on Machine Translation
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zhu-etal-2024-enhancing
%X For this shared task, we have used several machine translation engines to produce translations (en ⇔ fr) by fine-tuning a dialog-oriented NMT engine and having NMT baseline translations post-edited with prompt engineering. Our objectives are to test the effectiveness of a fine-tuning strategy with help of a robust NMT model, to draw out a from-translation-to-post-editing pipeline, and to evaluate the strong and weak points of NMT systems.
%U https://aclanthology.org/2024.wmt-1.103
%P 1016-1022

Markdown (Informal)

[Enhancing Translation Quality: A Comparative Study of Fine-Tuning and Prompt Engineering in Dialog-Oriented Machine Translation Systems. Insights from the MULTITAN-GML Team](https://aclanthology.org/2024.wmt-1.103) (Zhu et al., WMT 2024)

• Enhancing Translation Quality: A Comparative Study of Fine-Tuning and Prompt Engineering in Dialog-Oriented Machine Translation Systems. Insights from the MULTITAN-GML Team (Zhu et al., WMT 2024)

ACL

• Lichao Zhu, Maria Zimina, Behnoosh Namdarzadeh, Nicolas Ballier, and Jean-Baptiste Yunès. 2024. Enhancing Translation Quality: A Comparative Study of Fine-Tuning and Prompt Engineering in Dialog-Oriented Machine Translation Systems. Insights from the MULTITAN-GML Team. In Proceedings of the Ninth Conference on Machine Translation, pages 1016–1022, Miami, Florida, USA. Association for Computational Linguistics.