  Unsupervised neural machine translation (UNMT) is beneficial especially for
low resource languages such as those from the Dravidian family. However, UNMT
systems tend to fail in realistic scenarios involving actual low resource
languages. Recent works propose to utilize auxiliary parallel data and have
achieved state-of-the-art results. In this work, we focus on unsupervised
translation between English and Kannada, a low resource Dravidian language. We
additionally utilize a limited amount of auxiliary data between English and
other related Dravidian languages. We show that unifying the writing systems is
essential in unsupervised translation between the Dravidian languages. We
explore several model architectures that use the auxiliary data in order to
maximize knowledge sharing and enable UNMT for distant language pairs. Our
experiments demonstrate that it is crucial to include auxiliary languages that
are similar to our focal language, Kannada. Furthermore, we propose a metric to
measure language similarity and show that it serves as a good indicator for
selecting the auxiliary languages.
