Second, to go beyond cohesive soft constraints, we investigate efficient algorithms for learning and decoding with source-side dependency tree reordering models. We propose a novel source-tree reordering model that exploits dependency subtree inside / outside movements and cohesive soft constraints. These movements and constraints enable us to efficiently capture the subtree-to-subtree transitions observed both in the source of word-aligned training data and in the decoding time. Representing subtree movements as features allows MERT to train the corresponding weights for these features relative to others in the model. Moreover, experimental results on English-{Iraqi, Spanish} show that we obtain improvements +0.8 BLEU and −1.4 TER on English-Spanish and +0.8 BLEU and −2.3 TER on English-Iraqi. 
