  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.
