The learning of dialogue strategies in spoken dialogue systems using reinforcement learning is a promising approach to acquire robust dialogue strategies. However, the trade-off between available dialogue data and information in the dialogue state either forces information to be excluded from the state representations or requires large amount of training data. In this paper, we propose to use d...

In a spoken dialogue system, the function of a dialogue manager is to select actions based on observed events and inferred beliefs. To formalize and optimize the action selection process, researchers have turned to reinforcement learning methods which represent the dynamics of a spoken dialogue as a fully or partially observable Markov Decision Process. Once represented as such, optimal policie...

Intelligent Tutoring Systems (ITSs) are now recognised as an interesting alternative for providing learning opportunities in various domains. The Reinforcement Learning (RL) approach has been shown reliable for finding efficient teaching strategies. However, similarly to other human-machine interaction systems such as spoken dialogue systems, ITSs suffer from a partial knowledge of the interloc...

Recent progress in the eld of spoken natural language understanding expanded the scope of spoken language systems to include mixed initiative dialogue. Currently there are no agreed upon theoretical foundations for the design of such systems. In this work we propose a stochastic model of computer-human interactions. This model can be used for learning and adaptation of the dialogue strategy and...

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 which incorporates user and situation models, making such dialogue adaptation possible. Manually developing a set of dialogue rules to accommodate all possible model combinations is very difficult and obstructs syst...

Spoken medical dialogue is a valuable source of information for patients and caregivers. This work presents a first step towards automatic analysis and summarization of spoken medical dialogue. We first abstract a dialogue into a sequence of semantic categories using linguistic and contextual features integrated in a supervised machine-learning framework. Our model has a classification accuracy...

Spoken dialogue systems provide an opportunity for man machine interaction using spoken language as the medium of interaction. In recent years reinforcement learning-based dialogue policy optimization has evolved to be state of the art. In order to cope with the data requirement for policy optimization and also to evaluate dialogue policies user simulators are introduced. Almost all existing da...

One of the core components of modern spoken dialogue systems is the belief tracker, which estimates the user’s goal at every step of the dialogue. However, most current approaches have difficulty scaling to larger, more complex dialogue domains. This is due to their dependency on either: a) Spoken Language Understanding models that require large amounts of annotated training data; or b) hand-cr...

This paper describes NJFun, a real-time spoken dialogue systemthat-provides users with information about things to d~ in New Jersey. NJFun automatically optimizes its dialogue strategy over time, by using a methodology for applying reinforcement learning to a working dialogue system with human