Multilingual neural machine translation models (MNMT) yield state-of-the-art performance when evaluated on data from a domain and language pair seen at training time. However, when a MNMT model is used to translate under domain shift or to a new language pair, performance drops dramatically. We consider a very challenging scenario: adapting the MNMT model both to a new domain and to a new language pair at the same time. In this paper, we propose $m^4Adapter$ (Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter), which combines domain and language knowledge using meta-learning with adapters. We present results showing that our approach is a parameter-efficient solution which effectively adapts a model to both a new language pair and a new domain, while outperforming other adapter methods. An ablation study also shows that our approach more effectively transfers domain knowledge across different languages and language information across different domains.

翻译：多语言神经机器翻译模型(MNMT)在对培训时所见域对和语文对数据进行评估时产生最先进的性能。 但是,当使用MNMT模型来翻译域变换或新语言配对时,性能会急剧下降。我们考虑到一个非常具有挑战性的设想:将MNMT模型既调整到一个新的域,又同时调整到一个新的语文配对。在本文中,我们提议$m ⁇ 4Adapter$(Meta-Adapter的多语言机器翻译多语言适应),该模型将域学和语言知识与适应者结合起来。我们提出的结果显示,我们的方法是一种具有参数效率的解决方案,它有效地使模型适应新的语文配对和新领域,同时优于其他的适配方法。一个反研究还表明,我们的方法更有效地将不同语文和不同领域的语言信息转让域知识。