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.
Fields of Study & Subjects
Natural Language Processing Techniques
Topic Modeling
Text Readability and Simplification
Publication details
- Journal / source
- Proceedings of the Language Resources and Evaluation Conference
- Publisher
- European Language Resources Association (ELRA)
- Pages
- 482–490
- Publication date
- 1 May 2026