首个系统研究古埃及语到法语翻译的论文，提出有效低资源古语言翻译策略。
Neural Machine Translation for Coptic-French: Strategies for Low-Resource Ancient Languages
- 用圣经双语语料库，对比直接翻译与中间语言翻译效果
- 风格多样且含噪声的训练数据提升翻译质量
- 适合历史语言翻译研究者参考
本文首次系统研究将科普特语翻译为法语的策略。通过构建完整的处理流程，系统评估了直接翻译与中介翻译的差异、预训练的影响、多版本微调的优势以及模型对噪声的鲁棒性。基于对齐的圣经语料库，我们发现使用风格多样且包含噪声的训练数据进行微调，可显著提升翻译质量。研究成果为开发历史语言翻译工具提供了关键实践指导。
原文摘要 · Abstract (English)
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.
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