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.
REF integrated domain-specific lexicons in the translation model of a SMT engine which yields a significant reduction in word error rate.
Improving A General-Purpose Statistical Translation Engine By Terminological Lexicons
CompuTerm International Workshop On Computational Terminology
Computer Science