General-purpose statistical translation engine and domain specific texts

The past decade has witnessed exciting work in the field of Statistical Machine Translation (SMT). However, accurate evaluation of its potential in real-life contexts is still an open question. In this study, we investigate the behavior of an SMT engine faced with a corpus far different from the one it has been trained on. We show that terminological databases are obvious resources that should be used to boost the performance of a statistical engine. We propose and evaluate one way of integrating terminology into a SMT engine which yields a significant reduction in word error rate.