Performance Gain in Low Resource MT with Transfer Learning: An Analysis concerning Language Families

Abstract

Translation systems require a huge amount of parallel data to produce quality translations, but acquiring one for low-resource languages is difficult. To counter this, recent research has been shown to combine languages and use them to augment the low resource data, through transfer learning. While the gain in performance is apparent using transfer learning, we try to investigate the correlation between the performance gain and position of the concerned languages within a language family. We further probe and try to coordinate the performance gain with the degree of vocabulary sharing between the concerned languages.