When translating among languages that differ substantially in word order, machine translation (MT) systems beneﬁt from syntactic preordering—an approach that uses features from a syntactic parse to permute source words into a target-language-like order. This paper presents a method for inducing parse trees automatically from a parallel corpus, instead of using a supervised parser trained on a treebank. These induced parses are used to preorder source sentences. We demonstrate that our induced parser is effective: it not only improves a state-of-the-art phrase-based system with integrated reordering, but also approaches the performance of a recent preordering method based on a supervised parser. These results show that the syntactic structure which is relevant to MT pre-ordering can be learned automatically from parallel text, thus establishing a new application for unsupervised grammar induction.