Cognitively-inspired representational approach to meaning in machine dialogue

Abstract

© 2014 Elsevier Ltd. All rights reserved. One of the most fundamental research questions in the field of human-machine interaction is how to enable dialogue systems to capture the meaning of spontaneously produced linguistic inputs without explicit syntactic expectations. This paper introduces a cognitively-inspired representational model intended to address this research question. To the extent that this model is cognitively-inspired, it integrates insights from behavioral and neuroimaging studies on working memory operations and language-impaired patients (i.e., Broca's aphasics). The level of detail contained in the specification of the model is sufficient for a computational implementation, while the level of abstraction is sufficient to enable generalization of the model over different interaction domains. Finally, the paper reports on a domain-independent framework for end-user programming of adaptive dialogue management modules.