  We propose a straightforward vocabulary adaptation scheme to extend the
language capacity of multilingual machine translation models, paving the way
towards efficient continual learning for multilingual machine translation. Our
approach is suitable for large-scale datasets, applies to distant languages
with unseen scripts, incurs only minor degradation on the translation
performance for the original language pairs and provides competitive
performance even in the case where we only possess monolingual data for the new
languages.
