A Discriminative Syntactic Word Order Model for Machine Translation

We present a global discriminative statistical word order model for machine translation. Our model combines syntactic movement and surface movement information, and is discriminatively trained to choose among possible word orders. We show that com- bining discriminative training with features to detect these two different kinds of move- ment phenomena leads to substantial im- provements in word ordering performance over strong baselines. Integrating this word order model in a baseline MT system results in a 2.4 points improvement in BLEU for English to Japanese translation.