We combine the strengths of Bayesian modeling and synchronous grammar in unsupervised learning of basic translation phrase pairs. The structured space of a synchronous grammar is a natural fit for phrase pair probability estimation, though the search space can be prohibitively large. Therefore we explore efficient algorithms for pruning this space that lead to empirically effective results.