Paper: Learning Bilingual Linguistic Reordering Model for Statistical Machine Translation

ACL ID N09-1029
Title Learning Bilingual Linguistic Reordering Model for Statistical Machine Translation
Venue Human Language Technologies
Session Main Conference
Year 2009
Authors

In this paper, we propose a method for learn- ing reordering model for BTG-based statisti- cal machine translation (SMT). The model focuses on linguistic features from bilingual phrases. Our method involves extracting reor- dering 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 maxi- mum entropy (ME) reordering model. With the model, we performed Chinese-to-English translation tasks. Experimental results show that our bilingual linguistic model outper- forms the state-of-the-art phrase-based and BTG-based SMT systems by improvements of 2.41 and 1.31 BLEU points respectively.