State-transition cost functions and an application to language translation

We define a general method for ranking the solutions of a search process by associating costs with equivalence classes of state transitions of the process. We show how the method accommodates models based on probabilistic, discriminative, and distance cost functions, including assignment of costs to unseen events. By applying the method to our machine translation prototype, we are able to experiment with different cost functions and training procedures, including an unsupervised procedure for training the numerical parameters of our English-Chinese translation model. Results from these experiments show that the choice of cost function leads to significant differences in translation quality.