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 combining discriminative training with features to detect these two different kinds of movement phenomena leads to substantial improvements 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.

• Type: Conference
• Year: 2007
• Where: ACL
• Authors: Pi-Chuan Chang, Kristina Toutanova
• Keywords: ACL 2007, Combines Syntactic Movement, Computational Linguistics, Discriminative Statistical Word, Word Order Model