In this work we conduct a systematic study of the effects of backtranslated data from different sources, as well as how to optimally select subsets of this data taking into account the loss in quality and lexical richness when data is translated with different MT systems.
we aim to (i) provide a systematic analysis of backtranslated data from different sources; and (ii) to exploit a reduction in the amount of training data while maintaining high translation quality.