Learning Stochastic Bracketing Inversion Transduction Grammars with a Cubic Time Biparsing Algorithm

We present a biparsing algorithm for Stochastic Bracketing Inversion Transduction Grammars that runs in O(bn3) time instead of O(n6). Transduction grammars learned via an EM estimation procedure based on this biparsing algorithm are evaluated directly on the translation task, by building a phrase-based statistical MT system on top of the alignments dictated by Viterbi parses under the induced bigrammars. Translation quality at different levels of pruning are compared, showing improvements over a conventional word aligner even at heavy pruning levels.