Modeling the behavior of the dialogue management in the design of a spoken dialogue system using statistical methodologies is currently a growing research area. This paper presents a work on developing an adaptive learning approach to optimize dialogue strategy. At the core of our system is a method formalizing dialogue management as a sequential decision making under uncertainty whose underlyi...

Designing the dialogue strategy of a spoken dialogue system involves many nontrivial choices. This paper presents a reinforcement learning approach for automatically optimizing dialogue strategy. We first present a practical methodology that addresses the technical challenges in applying reinforcement learning to a working dialogue system with human users. We then demonstrate how we have used t...

The reinforcement learning paradigm has been adopted for inferring optimized and adaptive spoken dialogue agents. Such agents are typically learnt and tested without combining competing agents that may yield better performance at some points in the conversation. This paper presents an approach that learns dialogue behaviour from competing agents—switching from one policy to another competing on...

We have developed a discourse level tagging tool for spoken dialogue corpus using machine learning methods. As discourse level information, we focused on dialogue act, relevance and discourse segment. In dialogue act tagging, we have implemented a transformation-based learning procedure and resulted in 70% accuracy in open test. In relevance and discourse segment tagging, we have implemented a ...

The new Interactive Pattern Recognition (IPR) framework has been proposed to deal with human-machine interaction. In this context a new formulation has been recently defined to represent a Spoken Dialogue System as an IPR problem. In this work this formulation is applied to define graphical models that deal with Spoken Dialogue Systems. The definition of both a Dialogue Manager and a User Model...

This paper presents a machine learning-based approach to the incremental understanding of dialogue utterances, with a focus on the recognition of their communicative functions. A token-based approach combining the use of local classifiers, which exploit local utterance features, and global classifiers which use the outputs of local classifiers applied to previous and subsequent tokens, is shown...

This paper describes work in progress on DEAL, a spoken dialogue system under development at KTH. It is intended as a platform for exploring the challenges and potential benefits of combining elements from computer games, dialogue systems and language learning.

Speech recognition errors have been shown to negatively correlate with user satisfaction in evaluations of task-oriented spoken dialogue systems. In the domain of tutorial dialogue systems, however, where the primary evaluation metric is student learning, there has been little investigation of whether speech recognition errors also negatively correlate with learning. In this paper we examine co...

Speech recognition errors have been shown to negatively correlate with user satisfaction in evaluations of task-oriented spoken dialogue systems. In the domain of tutorial dialogue systems, however, where the primary evaluation metric is student learning, there has been little investigation of whether speech recognition errors also negatively correlate with learning. In this paper we examine co...

To provide a high level of usability, spoken dialogue systems must generate cooperative responses for a wide variety of users and situations. We introduce a dialogue planning scheme incorporating user and situation models making such dialogue adaptation possible. Manually developing a set of dialogue rules to account for all possible model combinations, would be very difficult and obstruct syst...