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Neural Machine Translation for Coptic-French: Strategies for Low-Resource Ancient Languages
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This paper presents the first systematic study of strategies for translating Coptic into French. Our comprehensive pipeline systematically evaluates: pivot versus direct translation, the impact of pre-training, the benefits of multi-version fine-tuning, and model robustness to noise. Utilizing aligned biblical corpora, we demonstrate that fine-tuning with a stylistically-varied and noise-aware training corpus significantly enhances translation quality. Our findings provide crucial practical insights for developing translation tools for historical languages in general.
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Cited by 1 Pith paper
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Syntax as a Rosetta Stone: Universal Dependencies for In-Context Coptic Translation
Combining dictionary glosses with Universal Dependencies syntactic information in in-context learning produces new state-of-the-art Coptic-English translation results across model sizes.
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