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arXiv · 2508.10683

Neural Machine Translation for Coptic-French: Strategies for Low-Resource Ancient Languages

Nasma Chaoui·Richard Khoury
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Abstract

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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Nasma Chaoui, Richard Khoury. 2025-08-14. Neural Machine Translation for Coptic-French: Strategies for Low-Resource Ancient Languages. https://arxiv.org/abs/2508.10683

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