1. A machine implemented system for training feature weights in a statistical machine translation model, comprising: a processor configured to obtain a list of translation hypotheses and associated feature values, the associated feature values defining a multidimensional feature weight space, the processor further configured to set a current point in the multidimensional feature weight space to an initial value, choose a line in the feature weight space that passes through the current point, reset the current point to optimize one or more feature weights with respect to the line, set the current point to be a best point attained, prune the list of translation hypotheses based at least in part on a determination that a particular hypothesis has never been touched in optimizing the feature weights, and output the point ascertained to be the best point in the feature weight space; and a memory coupled to the processor for persisting data.