Domain adaptation is an important challenge for neural machine translation.
However, the traditional fine-tuning solution requires multiple extra training
and yields a high cost. In this paper, we propose a non-tuning paradigm,
resolving domain adaptation with a prompt-based method. Specifically, we
construct a bilingual phrase-level database and retrieve relevant pairs from it
as a prompt for the input sentences. By utilizing Retrieved Phrase-level
Prompts (RePP), we effectively boost the translation quality. Experiments show
that our method improves domain-specific machine translation for 6.2 BLEU
scores and improves translation constraints for 11.5% accuracy without
additional training.

本文探讨了一种在神经机器翻译中解决领域适应问题的新思路，通过构建双语短语级数据库并从其中检索相关的短语，作为输入句子的提示，从而提高翻译质量。实验结果表明，该方法可以使特定领域机器翻译 BLEU 分数提高 6.2 分，并且可以提高 11.5% 的翻译准确性，而无需额外的训练。