In this paper, we describe an empirical evaluation of an adaptive mixed initiative spoken dialogue system. We conducted two sets of experiments to evaluate the mixed initiative and automatic adaptation aspects of the system, and analyzed the resulting dialogues along three dimensions: performance factors, discourse features, and initiative distribution. Our results show that 1) both the mixed i...

Spoken dialogue management strategy optimization by means of Reinforcement Learning (RL) is now part of the state of the art. Yet, there is still a clear mismatch between the complexity implied by the required naturalness of dialogue systems and the inability of standard RL algorithms to scale up. Another issue is the sparsity of the data available for training in the dialogue domain which can ...

In this paper, the simulated annealing Q-learning (SA-Q) algorithm is adopted to automatically learn the optimal dialogue strategy of a spoken dialogue system. Several simulations and experiments considering different user behaviors and speech recognizer performance are conducted to verify the effectiveness of the SA-Q learning approach. Moreover, the automatically learned strategy is applied t...

Developing dialogue systems is a complex process. In particular, designing efficient dialogue management strategies is often difficult as there are no precise guidelines to develop them and no sure test to validate them. Several suggestions have been made recently to use reinforcement learning to search for the optimal management strategy for specific dialogue situations. These approaches have ...

Currently the performance of dialogue system is mostly mearsured based on the analysis of a large dialogue corpus. In this way, the dialogue performance can not be obtained before the system is on line, and the dialogue corpus should be recollected if the system is modified. Also, the effect of different factors, including system’s dialogue strategy, recognition and understanding accuracy or us...

We aim to build dialogue agents that optimize the dialogue strategy, specifically through learning the dialogue model components from dialogue data. In this paper, we describe our current research on automatically learning dialogue strategies in the healthcare domain. We go through our systematic approach of learning dialogue model components from data, specifically user intents and the user mo...

Recently the technology for speech recognition and language processing for spoken dialogue systems has been improved, and speech recognition systems and dialogue systems have been developed to the extent of practical usage. In order to become more practical, not only those fundamental techniques but also the techniques of portability and expansibility should be developed. In our previous resear...

We propose a new approach to developing a tractable affective dialogue model for general probabilistic frame-based dialogue systems. The dialogue model, based on the Partially Observable Markov Decision Process (POMDP) and the Dynamic Decision Network (DDN) techniques, is composed of two main parts, the slot level dialogue manager and the global dialogue manager. Our implemented dialogue manage...

Dialogue modelling attempts to determine the way in which a dialog is developed. The dialogue strategy (i.e., the system behaviour) of an automatic dialogue system is determined by the dialogue model. Most dialogue systems use rule-based dialogue strategies, but recently, the probabilistic models have become very promising. We present probabilistic models based on the dialogue act concept, whic...