m4 Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter
Wen‐Cheng Lai, Alexandra Chronopoulou, Alexander D. Fraser · 2022
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 4 Adapter (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. 1