Discriminative Training of Natural Language Call Barriers

This paper shows how discriminative training can significantly improve classifiers used in natural language processing, using as an example the task of natural language call routing, where callers are transferred to desired departments based on natural spoken responses to an open-ended "How may I direct your call?" prompt. With vector-based natural language call routing, callers are transferred using a routing matrix trained on statistics of occurrence of words and word sequences in a training corpus. By re-training the routing matirx parameters using a minimum classification error criterion, a relative error rate reduction of 10-40% was achieved on a banking task.