In this paper, we develop a supervised learning technique that improves noisy phrase translation scores obtained by phrase table triangulation.
we extract word translation distributions from small amounts of source-target bilingual data (a dictionary or a parallel corpus) with which we learn to assign better scores to translation candidates obtained by triangulation.