Semantic filtering by inference on domain knowledge in spoken dialogue systems

TL;DR

This paper presents a method for improving spoken dialogue understanding by using domain knowledge and inference to filter and complete semantic interpretations, enhancing robustness and coverage.

Contribution

It introduces a semantic filtering approach leveraging domain knowledge and inference mechanisms to refine interpretation hypotheses in dialogue systems.

Findings

Semantic filtering reduces incorrect interpretations

Inference completes missing information in hypotheses

Improves robustness of spoken dialogue understanding

Abstract

General natural dialogue processing requires large amounts of domain knowledge as well as linguistic knowledge in order to ensure acceptable coverage and understanding. There are several ways of integrating lexical resources (e.g. dictionaries, thesauri) and knowledge bases or ontologies at different levels of dialogue processing. We concentrate in this paper on how to exploit domain knowledge for filtering interpretation hypotheses generated by a robust semantic parser. We use domain knowledge to semantically constrain the hypothesis space. Moreover, adding an inference mechanism allows us to complete the interpretation when information is not explicitly available. Further, we discuss briefly how this can be generalized towards a predictive natural interactive system.