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Domain adaptation is an important challenge for neural machine translation.However, the traditional fine-tuning solution requires multiple extra trainingand yields a high cost. In this paper, we propose a non-tuning paradigm,resolving domain adaptation with a prompt-based method. Specifically, weconstruct a bilingual phrase-level database and retrieve relevant pairs from itas a prompt for the input sentences. By utilizing Retrieved Phrase-levelPrompts (RePP), we effectively boost the translation quality. Experiments showthat our method improves domain-specific machine translation for 6.2 BLEUscores and improves translation constraints for 11.5% accuracy withoutadditional training.

Zero-shot Domain Adaptation for Neural Machine Translation with Retrieved Phrase-level Prompts