Generating a phrase translation model by iteratively estimating phrase translation probabilities

Abstract

A phrase translation model is trained without assuming a segmentation of training data into non-overlapping phrase pairs. Instead, the training algorithm assumes that any particular phrase instance has only a single phrase instance in another language as its translation in that instance, but that phrases can overlap. The model is trained by computing expected phrase alignment counts, deriving selection probabilities from current estimates of translation probabilities and then re-estimating phrase translation probabilities according to the expected phrase alignment counts computed. The model is trained by iterating over these steps until one or more desired stopping criteria are reached. The trained model can be deployed in a machine translation system.