Neural Machine Translation for Coptic-French: Strategies for Low-Resource Ancient Languages
Computation and Language
2026-05-14 v1
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.
Keywords
Cite
@article{arxiv.2508.10683,
title = {Neural Machine Translation for Coptic-French: Strategies for Low-Resource Ancient Languages},
author = {Nasma Chaoui and Richard Khoury},
journal= {arXiv preprint arXiv:2508.10683},
year = {2026}
}