  In this work, we investigate methods for the challenging task of translating
between low-resource language pairs that exhibit some level of similarity. In
particular, we consider the utility of transfer learning for translating
between several Indo-European low-resource languages from the Germanic and
Romance language families. In particular, we build two main classes of
transfer-based systems to study how relatedness can benefit the translation
performance. The primary system fine-tunes a model pre-trained on a related
language pair and the contrastive system fine-tunes one pre-trained on an
unrelated language pair. Our experiments show that although relatedness is not
necessary for transfer learning to work, it does benefit model performance.
