Synonym-Based Reordering Model for Statistical Machine Translation

Reordering model is the crucial component in statistical machine translation (SMT), since it plays an important role in the generation of fluent translation results. However, the data sparseness is the key factor that greatly affects the performance of reordering model in SMT. In this paper, we exploit synonymous information to alleviate the data sparseness and take Chinese-Mongolian SMT as example. First, a synonym-based reordering model with Chinese synonym is proposed for Chinese-Mongolian SMT. Then, we flexibly integrate synonym-based reordering model into baseline SMT as additional feature functions. Besides, we present source-side reordering as the pre-processing module to verify the extensibility of our synonym-based reordering model. Experiments on the Chinese-Mongolian dataset show that our synonym-based reordering model achieves significant improvement over baseline SMT system.