| Introduction | Most state-of—the-art statistical machine translation systems are based on large phrase tables extracted from parallel text using word-level alignments. |
| Introduction | These word-level alignments are most often obtained using Expectation Maximization on the conditional generative models of Brown et al. |
| Introduction | As these word-level alignment models restrict the word alignment complexity by requiring each target word to align to zero or one source words, results are improved by aligning both source-to-target as well as target-to-source, |
| Phrasal Inversion Transduction Grammar | Combining the two approaches, we have a staged training procedure going from the simplest unconstrained word based model to a constrained Bayesian word-level ITG model, and finally proceeding to a constrained Bayesian phrasal model. |