  Multilingual Neural Machine Translation (MNMT) for low-resource languages
(LRL) can be enhanced by the presence of related high-resource languages (HRL),
but the relatedness of HRL usually relies on predefined linguistic assumptions
about language similarity. Recently, adapting MNMT to a LRL has shown to
greatly improve performance. In this work, we explore the problem of adapting
an MNMT model to an unseen LRL using data selection and model adaptation. In
order to improve NMT for LRL, we employ perplexity to select HRL data that are
most similar to the LRL on the basis of language distance. We extensively
explore data selection in popular multilingual NMT settings, namely in
(zero-shot) translation, and in adaptation from a multilingual pre-trained
model, for both directions (LRL-en). We further show that dynamic adaptation of
the model's vocabulary results in a more favourable segmentation for the LRL in
comparison with direct adaptation. Experiments show reductions in training time
and significant performance gains over LRL baselines, even with zero LRL data
(+13.0 BLEU), up to +17.0 BLEU for pre-trained multilingual model dynamic
adaptation with related data selection. Our method outperforms current
approaches, such as massively multilingual models and data augmentation, on
four LRL.
