1. A method for routing calls comprising: receiving a call from a customer; extracting features from an utterance of the call; based on the extracted features, predicting a class and a complexity of a dialogue to be conducted between the customer and an agent, wherein the class c of the dialogue is computed according to Equation 1: p θ ⁡ ( c ❘ d ) = 1 1 + e - ( θ 0 c + θ 1 c · ϕ ⁡ ( d ) ) ( 1 ) where φ(d) is a feature vector derived from the features extracted from the utterance d; θ c vector of parameters to learn for each dialogue class c; and θ 0 c is a regularization vector; with a routing model, generating a routing strategy for steering the call to one of a plurality of types of agent, based on the predicted class and complexity of the dialogue and a cost assigned to the type of agent, a first of the plurality of types of agent being assigned a higher cost than a second of the types of agent; and outputting the routing strategy, wherein at least one of the extracting features, predicting the class and the complexity, and generating the routing strategy is performed with a processor.
1. A method for routing calls comprising: receiving a call from a customer; extracting features from an utterance of the call; based on the extracted features, predicting a class and a complexity of a dialogue to be conducted between the customer and an agent, wherein the class c of the dialogue is computed according to Equation 1: p θ ⁡ ( c ❘ d ) = 1 1 + e - ( θ 0 c + θ 1 c · ϕ ⁡ ( d ) ) ( 1 ) where φ(d) is a feature vector derived from the features extracted from the utterance d; θ c vector of parameters to learn for each dialogue class c; and θ 0 c is a regularization vector; with a routing model, generating a routing strategy for steering the call to one of a plurality of types of agent, based on the predicted class and complexity of the dialogue and a cost assigned to the type of agent, a first of the plurality of types of agent being assigned a higher cost than a second of the types of agent; and outputting the routing strategy, wherein at least one of the extracting features, predicting the class and the complexity, and generating the routing strategy is performed with a processor. 5. The method of claim 1 , wherein the extracted features comprise n-grams.