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.
@article{arxiv.2312.04807,
title = {Improving Neural Machine Translation by Multi-Knowledge Integration with Prompting},
author = {Ke Wang and Jun Xie and Yuqi Zhang and Yu Zhao},
journal= {arXiv preprint arXiv:2312.04807},
year = {2023}
}