Though the field of spoken dialogue systems has developed quickly in the last decade, rapid design of dialogue strategies remains uneasy. Several approaches to the problem of automatic strategy learning have been proposed and the use of Reinforcement Learning introduced by Levin and Pieraccini is becoming part of the state of the art in this area. However, the quality of the strategy learned by the system depends on the definition of the optimization criterion and on the accuracy of the environment model.

Turkish language is an agglutinative language. It is possible to produce a very high number of words from the same root with suffixes [1]. Language modeling for agglutinative languages needs to be different than modeling of languages like English. Such languages also have inflections but not as many as an agglutinative language. Techniques which can be used for modeling agglutinative languages are presented in this work.