A hybrid approach to dialogue management based on probabilistic rules. (November 2015)

• Record Type: Journal Article
• Authors: Lison, Pierre
• Is Part Of: Computer speech & language. Volume 34(2015)
• Page Start: 232
• Page End: 255
• Publication Date: 2015-11
• Subjects: Spoken dialogue systems -- Dialogue management -- Probabilistic graphical models -- Bayesian inference -- Human–robot interaction; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2015.01.001
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract : Highlights: We present a new, hybrid modelling framework for dialogue management based on probabilistic rules. The probabilistic rules function as high-level templates for the generation of a directed graphical model. The rule parameters may be estimated from dialogue data via Bayesian inference. The OpenDial toolkit allows system designers to develop dialogue systems using probabilistic rules. User evaluation in a HRI domain shows that the approach outperforms traditional hand-crafted and statistical models. Abstract: We present a new modelling framework for dialogue management based on the concept of probabilistic rules . Probabilistic rules are defined as structured mappings between logical conditions and probabilistic effects. They function as high-level templates for probabilistic graphical models and may include unknown parameters whose values are estimated from data using Bayesian inference. Thanks to their use of logical abstractions, probabilistic rules are able to encode the probability and utility models employed in dialogue management in a compact and human-readable form. As a consequence, they can reduce the amount of dialogue data required for parameter estimation and allow system designers to directly incorporate their expert domain knowledge into the dialogue models. Empirical results of a user evaluation in a human–robot interaction task with 37 participants show that a dialogue manager structured with probabilistic rules outperforms both purely hand-crafted and purely statistical methods on a range of subjective and objective quality metrics. The framework is implemented in a software toolkit called OpenDial, which can be used to develop various types of dialogue systems based on probabilistic rules.