Domain adaptation for statistical machine translation

Statistical machine translation (SMT) plays more and more important role now. The performance of the SMT is largely dependent on the size and quality of training data. But the demands for translation is rich, how to make the best of limited in-domain data to satisfy the needs of translation coming from different domains is one of the hot focus in current SMT. Domain adaption aims to obviously improve the specific-domain performance by bringing much out-of-domain parallel corpus at the absence of in-domain parallel corpus. Domain adaption is one of the keys to get the SMT into practical application. This paper introduces mainstream methods of domain adaption for SMT, compares advantages and disadvantages of representative methods based on the result of the same data and shows personal views about the possible future direction of domain adaption for SMT.