Sampling Alignment Structure under a Bayesian Translation Model

We describe the first tractable Gibbs sampling procedure for estimating phrase pair frequencies under a probabilistic model of phrase alignment. We propose and evaluate two nonparametric priors that successfully avoid the degenerate behavior noted in previous work, where overly large phrases memorize the training data. Phrase table weights learned under our model yield an increase in BLEU score over the word-alignment based heuristic estimates used regularly in phrasebased translation systems.

• Type: Conference
• Year: 2008
• Where: EMNLP
• Authors: John DeNero, Alexandre Bouchard-Côté, Dan Klein
• Keywords: EMNLP 2008, Natural Language Processing, Phrase Alignment, Phrase Pair Frequencies, Phrase Table Weights