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.

翻译：近年来，通过提示方法改进神经机器翻译（NMT）系统取得了显著进展。本研究聚焦于如何将多知识（多种类型的知识）有效融入NMT模型，以通过提示机制提升翻译性能。我们提出了一种统一框架，能够将包括句子、术语/短语以及翻译模板在内的多种类型知识有效整合到NMT模型中。该方法利用多种类型的知识作为编码器与解码器的输入前缀提示，从而引导整个翻译过程。该方案无需改动模型架构，且能无需重训练即可高效适配领域特定翻译任务。在英汉与英德翻译任务上的实验表明，我们的方法显著超越强基线模型，实现了高质量翻译与术语匹配准确率的双重提升。