12. A translation method, comprising: recognizing an utterance using a plurality of models each being employed to decode the utterance to provide an output; assigning probabilities to the outputs based on an exponentiated and normalized loss accumulated over time; weighting the models based upon the assigned probabilities; using a processor to predict a best performing model based on the weighting of the outputs; and determining a combination hypothesis for the best performing model and applying at least one of past performance and user input as feedback to adjust the weights for translating a next utterance.
18. The method as recited in claim 12 , wherein the probabilities are computed based on machine translation models.