We describe a transformation-based learning method for learning a sequence of mono-lingual tree transformations that improve the agreement between constituent trees and word alignments in bilingual corpora. Using the manually annotated English Chinese Transla-tion Treebank, we show how our method au-tomatically discovers transformations that ac-commodate differences in English and Chi-nese syntax. Furthermore, when transforma-tions are learned on automatically generated trees and alignments from the same domain as the training data for a syntactic MT system, the transformed trees achieve a 0.9 BLEU im-provement over baseline trees. 1