Trung H. Bui

A tractable hybrid ddn–pomdp approach to affective dialogue modeling for probabilistic frame-based dialogue systems

We propose a novel approach to developing a tractable affective dialogue model for probabilistic frame-based dialogue systems. The affective dialogue model, based on Partially Observable Markov Decision Process (POMDP) and Dynamic Decision Network (DDN) techniques, is composed of two main parts: the slot-level dialogue manager and the global dialogue manager. It has two new features: (1) being able to deal with a large number of slots and (2) being able to take into account some aspects of the users affective state in deriving the adaptive dialogue strategies. Our implemented prototype dialogue manager can handle hundreds of slots, where each individual slot might have hundreds of values. Our approach is illustrated through a route navigation example in the crisis management domain. We conducted various experiments to evaluate our approach and to compare it with approximate POMDP techniques and handcrafted policies. The experimental results showed that the DDN–POMDP policy outperforms three handcrafted policies when the users action error is induced by stress as well as when the observation error increases. Further, performance of the one-step look-ahead DDN–POMDP policy after optimizing its internal reward is close to state-of-the-art approximate POMDP counterparts.

Rapid Dialogue Prototyping Methodology

This paper is about the automated production of dialogue models. The goal is to propose and validate a methodology that allows the production of finalized dialogue models (i.e. dialogue models specific for given applications) in a few hours. The solution we propose for such a methodology, called the Rapid Dialogue Prototyping Methodology (RDPM), is decomposed into five consecutive main steps, namely: (1) producing the task model; (2) deriving the initial dialogue model; (3) using a Wizard-of-Oz experiment to instantiate the initial dialogue model; (4) using an internal field test to refine the dialogue model; and (5) using an external field test to evaluate the final dialogue model. All five steps will be described in more detail in the document.