In this paper, we develop a supervised learning algorithm that corrects triangulated word translation probabilities by relying on word translation distributions w sup derived from the limited sourcetarget data.
we represent source and target words using word embeddings (Mikolov et al., 2013) and learn a transformation between the two embedding spaces in order to approximate w sup , thus down-weighting incorrect translation candidates proposed by triangulation ( §3).