Theoretically, Bayesian models should outperform maximum likelihood approaches on small data sets, due to their improved modelling of un-(a) GIZA++ baseline (b) our model certainty.
in this paper we limit our focus to inducing word alignments, i.e., by using the model to infer alignments which are then used in a standard phrasebased translation pipeline.