Improving the IBM Alignment Models Using Variational Bayes

Bayesian approaches have been shown to reduce the amount of overfitting that occurs when running the EM algorithm, by placing prior probabilities on the model parameters. We apply one such Bayesian technique, variational Bayes, to the IBM models of word alignment for statistical machine translation. We show that using variational Bayes improves the performance of the widely used GIZA++ software, as well as improving the overall performance of the Moses machine translation system in terms of BLEU score.

• Type: Journal
• Year: 2012
• Where: ACL
• Authors: Darcey Riley, Daniel Gildea
• Keywords: ACL 2012, Computational Linguistics, Machine Translation System, Statistical Machine Translation, Word Alignment