Paper: Sampling Alignment Structure under a Bayesian Translation Model

ACL ID D08-1033
Title Sampling Alignment Structure under a Bayesian Translation Model
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008
Authors

We describe the first tractable Gibbs sam- pling procedure for estimating phrase pair frequencies under a probabilistic model of phrase alignment. We propose and evalu- ate two nonparametric priors that successfully avoid the degenerate behavior noted in previ- ous work, where overly large phrases mem- orize 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 phrase- based translation systems.