Paper: A Systematic Comparison of Training Criteria for Statistical Machine Translation

ACL ID D07-1055
Title A Systematic Comparison of Training Criteria for Statistical Machine Translation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors

We address the problem of training the free parameters of a statistical machine transla- tion system. We show significant improve- ments over a state-of-the-art minimum er- ror rate training baseline on a large Chinese- English translation task. We present novel training criteria based on maximum likeli- hood estimation and expected loss compu- tation. Additionally, we compare the maxi- mum a-posteriori decision rule and the min- imum Bayes risk decision rule. We show that, not only from a theoretical point of view but also in terms of translation qual- ity, the minimum Bayes risk decision rule is preferable.