Narrow Results

Word ordering is among the most important problems in machine translation. In this paper, we describe a general approach to solve this problem in English-Vietnamese- English statistical machine translation. Our model automatically extracts short-range and long-range reordering rules based on part-of-speech tags and alignment information. Our method, therefore, covers both local and global word order, and is more versatile than other methods. To obtain a better set of reordering rules, we omit generated rules if their weight is lower than a threshold $\alpha$. The experimental results have shown that the translation quality has been improved significantly compared to the distance-based reordering model and comparable to the lexicalized model. Our approach is not only suitable for English-Vietnamese but also for language pairs which have many differences in syntax, such as English-Chinese and Chinese-Vietnamese.