Improving a general-purpose Statistical Translation Engine by terminological lexicons
Philippe Langlais · 2002
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 a questionable issue.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 a way of integrating terminology into a SMT engine which yields a significant reduction in word error rate.