Learning Bilingual Linguistic Reordering Model for Statistical Machine Translation

In this paper, we propose a method for learning reordering model for BTG-based statistical machine translation (SMT). The model focuses on linguistic features from bilingual phrases. Our method involves extracting reordering examples as well as features such as part-of-speech and word class from aligned parallel sentences. The features are classified with special considerations of phrase lengths. We then use these features to train the maximum entropy (ME) reordering model. With the model, we performed Chinese-to-English translation tasks. Experimental results show that our bilingual linguistic model outperforms the state-of-the-art phrase-based and BTG-based SMT systems by improvements of 2.41 and 1.31 BLEU points respectively.