Simplifying design specification for automatic training of robust natural language call router

We study techniques that allow us to relax some constraints imposed by expert knowledge in task specifications of a natural language call router design. We intend to fully automate the training of the routing matrix while still maintaining the same level of performance (over 90% accuracy) as that in an optimized system. Two specific issues are investigated: (1) reducing the matrix size by removing word pairs and triplets in key term definition while using only single word terms; and (2) increasing the matrix size by removing the need for defining stop words and performing stop word filtering. Since simplification of design often implies a degradation of performance, discriminative training of routing matrix parameters becomes an essential procedure. We show in our experiments that the performance degradation caused by relaxing the design constraints can be compensated entirely by minimum error classification (MCE) training even with the above two simplifications. We believe the procedure is applicable to algorithms addressing a broad range of speech understanding, topic identification, and information retrieval problems