Neural Machine Translation for Coptic-French: Strategies for Low-Resource Ancient Languages
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909038131806208 |
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| author | Chaoui, Nasma Khoury, Richard |
| author_facet | Chaoui, Nasma Khoury, Richard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10683 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Machine Translation for Coptic-French: Strategies for Low-Resource Ancient Languages Chaoui, Nasma Khoury, Richard Computation and Language 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. |
| title | Neural Machine Translation for Coptic-French: Strategies for Low-Resource Ancient Languages |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2508.10683 |