Using Model-Theoretic Semantic Interpretation to Guide Statistical Parsing and Word Recognition in a Spoken Language Interface

This paper describes an extension of the semantic grammars used in conventional statistical spoken language interfaces to allow the probabilities of derived analyses to be conditioned on the meanings or denotations of input utterances in the context of an interface's underlying application environment or world model. Since these denotations will be used to guide disambiguation in interactive applications, they must be efciently shared among the many possible analyses that may be assigned to an input utterance. This paper therefore presents a formal restriction on the scope of variables in a semantic grammar which guarantees that the denotations of all possible analyses of an input utterance can be calculated in polynomial time, without undue constraints on the expressivity of the derived semantics. Empirical tests show that this model-theoretic interpretation yields a statistically signi cant improvement on standard measures of parsing accuracy over a baseline grammar not conditioned on denotations.