8th European Conference on Speech Communication and Technology

Geneva, Switzerland
September 1-4, 2003


A Trainable Generator for Recommendations in Multimodal Dialog

Marilyn Walker (1), Rashmi Prasad (2), Amanda Stent (3)

(1) University of Sheffield, U.K.
(2) University of Pennsylvania, USA
(3) Stony Brook University, USA

As the complexity of spoken dialogue systems has increased, there has been increasing interest in spoken language generation (SLG). SLG promises portability across application domains and dialogue situations through the development of application-independent linguistic modules. However in practice, rule-based 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.

Full Paper

Bibliographic reference.  Walker, Marilyn / Prasad, Rashmi / Stent, Amanda (2003): "A trainable generator for recommendations in multimodal dialog", In EUROSPEECH-2003, 1697-1700.