  Improving neural machine translation (NMT) systems with prompting has
achieved significant progress in recent years. In this work, we focus on how to
integrate multi-knowledge, multiple types of knowledge, into NMT models to
enhance the performance with prompting. We propose a unified framework, which
can integrate effectively multiple types of knowledge including sentences,
terminologies/phrases and translation templates into NMT models. We utilize
multiple types of knowledge as prefix-prompts of input for the encoder and
decoder of NMT models to guide the translation process. The approach requires
no changes to the model architecture and effectively adapts to domain-specific
translation without retraining. The experiments on English-Chinese and
English-German translation demonstrate that our approach significantly
outperform strong baselines, achieving high translation quality and terminology
match accuracy.
