小数据下结合回译与微调，显著提升日语翻译质量
Exploring Parameter-Efficient Fine-Tuning and Backtranslation for the WMT 25 General Translation Task
- 先用日语文本生成回译数据，再在真实小语料上微调模型
- 组合方法使COMET得分达0.597，优于单独使用任一技术
- 适合低资源语言对的轻量级高效翻译优化，尤其适用于日语
本文研究在小规模日语语料上结合微调与回译对神经机器翻译的效果。从基线英→日模型（COMET=0.460）出发，先利用单语日语文本生成回译数据，使性能小幅提升至COMET=0.468；随后在来自多样新闻与文学语料的小型平行语料上微调，使用Mistral 7B模型使性能大幅提升至COMET=0.589；最后将回译数据增强原始小语料，再进行微调，进一步提升至COMET=0.597。结果表明，即使数据有限，回译与针对性微调的协同作用也能显著提升翻译质量，优于单一方法。该策略为低资源语言对提供了轻量而高效的改进路径。
原文摘要 · Abstract (English)
In this paper, we explore the effectiveness of combining fine-tuning and backtranslation on a small Japanese corpus for neural machine translation. Starting from a baseline English{\textrightarrow}Japanese model (COMET = 0.460), we first apply backtranslation (BT) using synthetic data generated from monolingual Japanese corpora, yielding a modest increase (COMET = 0.468). Next, we fine-tune (FT) the model on a genuine small parallel dataset drawn from diverse Japanese news and literary corpora, achieving a substantial jump to COMET = 0.589 when using Mistral 7B. Finally, we integrate both backtranslation and fine-tuning{ -- }first augmenting the small dataset with BT generated examples, then adapting via FT{ -- }which further boosts performance to COMET = 0.597. These results demonstrate that, even with limited training data, the synergistic use of backtranslation and targeted fine-tuning on Japanese corpora can significantly enhance translation quality, outperforming each technique in isolation. This approach offers a lightweight yet powerful strategy for improving low-resource language pairs.
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