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.

通过将多种类型的知识，如句子、术语 / 短语和翻译模板有效地融入神经机器翻译 (NMT) 模型，我们提出了一个统一的框架，无需修改模型架构即可实现领域特定的翻译，显著提高了翻译质量和术语匹配准确性。

通过提示的多知识整合改进神经机器翻译

Improving Neural Machine Translation by Multi-Knowledge Integration with  Prompting

In this paper, we focus on the domain-specific translation with low
resources, where in-domain parallel corpora are scarce or nonexistent. One
common and effective strategy for this case is exploiting in-domain monolingual
data with the back-translation method. However, the synthetic parallel data is
very noisy because they are generated by imperfect out-of-domain systems,
resulting in the poor performance of domain adaptation. To address this issue,
we propose a novel iterative domain-repaired back-translation framework, which
introduces the Domain-Repair (DR) model to refine translations in synthetic
bilingual data. To this end, we construct corresponding data for the DR model
training by round-trip translating the monolingual sentences, and then design
the unified training framework to optimize paired DR and NMT models jointly.
Experiments on adapting NMT models between specific domains and from the
general domain to specific domains demonstrate the effectiveness of our
proposed approach, achieving 15.79 and 4.47 BLEU improvements on average over
unadapted models and back-translation.

本文提出了一种迭代式领域修复回译框架，通过域修复模型对合成双语数据进行优化，达到在特定领域适配 NMT 模型的效果提升。