A trainable generator for recommendations in multimodal dialog

As the complexity of spoken dialogue systems has increased, there has been increasing interest spoken language generation (SLG). SLG promises portability across application domains and dialogue situations through the development of applicationindependent linguistic modules. However in practice, rulebased SLGs often have to be tuned to the application. Recently, a number of research groups have been developing hybrid methods for spoken language generation, combining general linguistic modules with methods for training parameters for particular applications. This paper describes the use of boosting to train a sentence planner to generate recommendations for restaurants in MATCH, a multimodal dialogue system providing entertainment information for New York.