The general strategy is to propose a number of sets of biterminal rules and a place to segment them, estimate the posterior probability given these sets and commit to the best.
we introduce a minimalist, unsupervised learning model that induces relatively clean, compact phrasal translation lexicons by employing a novel Bayesian approach that attempts to find the maximum a posteriori (MAP) or minimum description length (MDL) model.