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

Nasma Chaoui, Richard Khoury
8/14/2025
cs.CL

No official code found. Best implementation: chaouin/coptic-french-nmt (0 stars, last commit 2025-08-07).

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.

View on arXivView PDF

Code Implementations(1)

chaouin/coptic-french-nmtCommunity69%

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

01PythonAug 7, 20251 years ago

Similar Papers

Investigating Neural Machine Translation for Low-Resource Languages: Using Bavarian as a Case Study

84% match1 repo52024

Improving Low-Resource Machine Translation via Cross-Linguistic Transfer from Typologically Similar High-Resource Languages

82% match1 repo2024

From LLM to NMT: Advancing Low-Resource Machine Translation with Claude

82% match2024

From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation

82% match1 repo22024

The Serendipity of Claude AI: Case of the 13 Low-Resource National Languages of Mali

81% match2025

The eBible Corpus: Data and Model Benchmarks for Bible Translation for Low-Resource Languages

81% match1 repo62023

Cite this paper

@article{chaoui2025neural,
  title  = {Neural Machine Translation for Coptic-French: Strategies for Low-Resource Ancient Languages},
  author = {Nasma Chaoui and Richard Khoury},
  year   = {2025},
  eprint = {2508.10683},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2508.10683v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2508.10683.svg)](https://codeofpaper.com/paper/2508.10683)

Discussion