We present a novel approach for induc-ing unsupervised dependency parsers for languages that have no labeled training data, but have translated text in a resource-rich language. We train probabilistic pars-ing models for resource-poor languages by transferring cross-lingual knowledge from resource-rich language with entropy reg-ularization. Our method can be used as a purely monolingual dependency parser, requiring no human translations for the test data, thus making it applicable to a wide range of resource-poor languages. We perform experiments on three Data sets — Version 1.0 and version 2.0 of Google Universal Dependency Treebanks and Treebanks from CoNLL shared-tasks, across ten languages. We obtain state-of-the art performance of all the three data sets when compared with previously studied unsupervised and projected pars-ing systems. 1