Improving Neural Machine Translation by Multi-Knowledge Integration with
  Prompting
Ke Wang Jun Xie Yuqi Zhang Yu Zhao
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.