Our results show that the different losses work in tandem to learn a better mapping than any one can on its own ( §4.2).
we show that two instantiations of the semi-supervised framework, corresponding to different supervised loss objectives, outperform their supervised and unsupervised counterparts on nu-merous language pairs across two datasets.