Title: Semantic modeling for dialog systems in a pattern recognition framework

Authors: Kuansan Wang

Abstract: In this paper, we describe a multimodal dialog system based on the pattern recognition framework that has been successfully applied to automatic speech recognition. We treat the dialog problem as to recognize the optimal action based on the user's input and context. In analogous to the acoustic, pronunciation, and language models for speech recognition, the dialog system in this framework has language, semantic, and behavior models to take into account when it searches for the best result. The paper focuses on our approaches in semantic modeling, describing how semantic lexicon and domain knowledge are derived and integrated. We show that, once semantic abstraction is introduced, multimodal integration can be achieved using the reference resolution algorithm developed for natural language understanding. Several applications developed to test various aspects of the proposed framework are also described.

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