  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.
