DIA-MOLE: An Unsupervised Learning Approach to Adaptive Dialogue Models for Spoken Dialogue Systems

Abstract

The DIAlogue MOdel Learning Environment supports an engineering-oriented approach towards dialogue modelling for a spoken-language interface. Major steps towards dialogue models is to know about the basic units that are used to construct a dialogue model and possible sequences. In difference to many other approaches a set of dialogue acts is not predefined by any theory or manually during the engineering process, but is learned from data that are available in an avised spoken dialogue system. The architecture is outlined and the approach is applied to the domain of appointment scheduling. Even though based on a word correctness of about 70% predictability of dialogue acts in DIA-MOLE turns out to be comparable to human-assigned dialogue acts.