1. A translation system comprising a computer useable storage medium including a computer readable program, wherein the computer readable program when executed on a computer, comprises: a recognition engine having a plurality of models each being employed to decode a same utterance to provide an output; and a model combiner, including a probability module configured to compute a probability for each output based on an exponentiated and normalized loss accumulated over time, configured to assign probabilities to each model output and configured to assign weights to the outputs of the plurality of models based on the probabilities to provide a best performing model for the context of the utterance.
1. A translation system comprising a computer useable storage medium including a computer readable program, wherein the computer readable program when executed on a computer, comprises: a recognition engine having a plurality of models each being employed to decode a same utterance to provide an output; and a model combiner, including a probability module configured to compute a probability for each output based on an exponentiated and normalized loss accumulated over time, configured to assign probabilities to each model output and configured to assign weights to the outputs of the plurality of models based on the probabilities to provide a best performing model for the context of the utterance. 11. The system as recited in claim 1 , wherein the probabilities are calculated as P ⁡ ( x j ⁡ ( t ) ) = exp ⁡ ( - c ⁢ ∑ i = 1 t - 1 ⁢ ⁢ l ⁡ ( x j ⁡ ( i ) ) ) ∑ k = 1 m ⁢ ⁢ exp ⁡ ( - c ⁢ ∑ i = 1 t - 1 ⁢ ⁢ l ⁢ ( x k ⁡ ( i ) ) ) , where x j (t) is an output of a given model, c is a learning parameter, l(x j (i)) represents the loss of the output, j=1, . . . , m, t is the time, and there are m different outputs.