Combination of boosting and discriminative training for natural language call steering systems

In this paper, we describe the combination of two different techniques to improve natural language call routing: boosting and discriminative training. The goal of boosting is to re-weight the data in order to train a set of classifiers whose errors may be uncorrelated so that when combined, the classification error rate (CER) can be reduced. We propose using discriminative training to improve the individual classifier accuracy at each iteration of the boosting algorithm. Compared to the baseline classifiers, an improvement in the CER of 41–50% was observed on call routing for a banking task. More importantly, synergistic effects of discriminative training on the boosting algorithm were demonstrated: more iterations were possible because discriminative training reduced the CER of individual classifiers trained on re-weighted data by an average of 72%.