Memory-based understanding of user utterances in a spoken dialogue system:Effects of feature selection and co-learning

Understanding user utterances in human-computer spoken dialogue systems involves a multi-level pragmatic-semantic analysis of noisy natural language input streams. These analyses are heavily dependent on the dialogue context, and are complex due to the inherent ambiguity of language use, and to the errors induced by the intermediate speech recognition system. We review work on applying k-nearest-neighbour classification to this multi-level task split into (1) dialogue act classification, (2) slot filling identification, and (3) communication problem signalling, showing that co-learning some of these tasks produces superior results over learning them in isolation. We also show that additional feature selection can produce succinct feature sets, illustrating the viability of simple keyword-based shallow understanding.

The Tully-Fisher Zero Point Problem

A long standing problem for hierarchical disk galaxy formation models has been the simultaneous matching of the zero point of the Tully-Fisher relation and the galaxy luminosity function (LF). We illustrate this problem for a typical disk galaxy and discuss three solutions: low stellar mass-to-light ratios, low initial dark halo concentrations, and no halo contraction. We speculate that halo contraction may be reversed through a combination of mass ejection through feedback and angular momentum exchange brought about by dynamical friction between baryons and dark matter during the disk formation process.Comment: 4 pages, 1 figure, to appear in proceedings of "Formation and Evolution of Galaxy Disks", Rome, October 2007, Eds. J.G. Funes, S.J. and E.M. Corsin