{"ID":2882367,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.10683","arxiv_id":"2508.10683","title":"Neural Machine Translation for Coptic-French: Strategies for Low-Resource Ancient Languages","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.","short_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,...","url_abs":"https://arxiv.org/abs/2508.10683","url_pdf":"https://arxiv.org/pdf/2508.10683v1","authors":"[\"Nasma Chaoui\",\"Richard Khoury\"]","published":"2025-08-14T14:25:34Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[]","has_code":false}