Joint Satisfaction of Syntactic and Pragmatic Constraints Improves Incremental Spoken Language Understanding

We present a model of semantic processing of spoken language that (a) is robust against ill-formed input, such as can be expected from automatic speech recognisers, (b) respects both syntactic and pragmatic constraints in the computation of most likely interpretations, (c) uses a principled, expressive semantic representation formalism (RMRS) with a well-defined model theory, and (d) works continuously (producing meaning representations on a wordby-word basis, rather than only for full utterances) and incrementally (computing only the additional contribution by the new word, rather than re-computing for the whole utterance-so-far). We show that the joint satisfaction of syntactic and pragmatic constraints improves the performance of the NLU component (around 10% absolute, over a syntax-only baseline).