Enhancing Translation Quality: A Comparative Study of Fine-Tuning and Prompt Engineering in Dialog-Oriented Machine Translation Systems. Insights from the MULTITAN-GML Team
Enhancing Translation Quality: A Comparative Study of Fine-Tuning and Prompt Engineering in Dialog-Oriented Machine Translation Systems. Insights from the MULTITAN-GML Team
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 through prompt engineering. Our objectives are to assess the effectiveness of a fine-tuning strategy with a robust NMT model, to advance towards a comprehensive pipeline that covers the entire translation process (from fine-tuning and machine translation to automatic post-editing (APE)), and to evaluate the strengths and weaknesses of NMT systems.
- UNIVERSITE PARIS DESCARTES France
- University of Paris France
- Laboratoire de Linguistique Formelle France
- Université Paris Cité (UMR 8240) France
- Panthéon-Assas University France
LLM, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Fine-tuning, NMT Neural Machine Translation, [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, Retrieval Augmented Generation RAG, [SCCO.LING] Cognitive science/Linguistics, Post-editing strategies, [SHS.LANGUE] Humanities and Social Sciences/Linguistics, Specialized translation
LLM, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Fine-tuning, NMT Neural Machine Translation, [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, Retrieval Augmented Generation RAG, [SCCO.LING] Cognitive science/Linguistics, Post-editing strategies, [SHS.LANGUE] Humanities and Social Sciences/Linguistics, Specialized translation
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