Neural Machine Translation for Coptic-French: Strategies for Low-Resource Ancient Languages
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 17 references that are in the map. First posted on arXiv 14 Aug 2025.
Builds on
7 of 17 references.
References · 7
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
- 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,203
- 2020COMET: A Neural Framework for MT Evaluationcited by 1,033
- 2021Learning Compact Metrics for MTcited by 93
- 2022Democratizing Neural Machine Translation with OPUS-MTcited by 58
- 2024Lacuna Language Learning: Leveraging RNNs for Ranked Text Completion in Digitized Coptic Manuscriptscited by 1
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Citing papers per year: 2026: 1.
1 paper.