  Transfer learning is a popular strategy to improve the quality of
low-resource machine translation. For an optimal transfer of the embedding
layer, the child and parent model should share a substantial part of the
vocabulary. This is not the case when transferring to languages with a
different script. We explore the benefit of romanization in this scenario. Our
results show that romanization entails information loss and is thus not always
superior to simpler vocabulary transfer methods, but can improve the transfer
between related languages with different scripts. We compare two romanization
tools and find that they exhibit different degrees of information loss, which
affects translation quality. Finally, we extend romanization to the target
side, showing that this can be a successful strategy when coupled with a simple
deromanization model.
