  This work focuses on comparing different solutions for machine translation on
low resource language pairs, namely, with zero-shot transfer learning and
unsupervised machine translation. We discuss how the data size affects the
performance of both unsupervised MT and transfer learning. Additionally we also
look at how the domain of the data affects the result of unsupervised MT. The
code to all the experiments performed in this project are accessible on Github.
