Improving Neural Machine Translation by Multi-Knowledge Integration with Prompting

• URL: http://arxiv.org/abs/2312.04807v1
• Date: Fri, 8 Dec 2023 02:55:00 GMT
• Title: Improving Neural Machine Translation by Multi-Knowledge Integration with Prompting
• Authors: Ke Wang, Jun Xie, Yuqi Zhang, Yu Zhao
• Abstract summary: 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.
• Score: 36.24578487904221
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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