  Multilingual language models have shown impressive cross-lingual transfer
ability across a diverse set of languages and tasks. To improve the
cross-lingual ability of these models, some strategies include transliteration
and finer-grained segmentation into characters as opposed to subwords. In this
work, we investigate lexical sharing in multilingual machine translation (MT)
from Hindi, Gujarati, Nepali into English. We explore the trade-offs that exist
in translation performance between data sampling and vocabulary size, and we
explore whether transliteration is useful in encouraging cross-script
generalisation. We also verify how the different settings generalise to unseen
languages (Marathi and Bengali). We find that transliteration does not give
pronounced improvements and our analysis suggests that our multilingual MT
models trained on original scripts seem to already be robust to cross-script
differences even for relatively low-resource languages
