  We study the selection of transfer languages for different Natural Language
Processing tasks, specifically sentiment analysis, named entity recognition and
dependency parsing. In order to select an optimal transfer language, we propose
to utilize different linguistic similarity metrics to measure the distance
between languages and make the choice of transfer language based on this
information instead of relying on intuition. We demonstrate that linguistic
similarity correlates with cross-lingual transfer performance for all of the
proposed tasks. We also show that there is a statistically significant
difference in choosing the optimal language as the transfer source instead of
English. This allows us to select a more suitable transfer language which can
be used to better leverage knowledge from high-resource languages in order to
improve the performance of language applications lacking data. For the study,
we used datasets from eight different languages from three language families.
