Dialogue act classification is an important step in understanding students’ utterances within tutorial dialogue systems. Machinelearned models of dialogue act classification hold great promise, and among these, unsupervised dialogue act classifiers have the great benefit of eliminating the human dialogue act annotation effort required to label corpora. In contrast to traditional evaluation appr...

Natural language interfaces require dialogue models that allow for robust, habitable and eÆcient interaction. This paper presents such a model for dialogue management for natural language interfaces. The model is based on empirical studies of human computer interaction in various simple service applications. It is shown that for applications belonging to this class the dialogue can be handled u...

Unsupervised machine learning approaches hold great promise for recognizing dialogue acts, but the performance of these models tends to be much lower than the accuracies reached by supervised models. However, some dialogues, such as task-oriented dialogues with parallel task streams, hold rich information that has not yet been leveraged within unsupervised dialogue act models. This paper invest...

While Spoken Dialogue Systems have gained in importance in recent years, most systems applied in the real world are still static and error-prone. To overcome this, the user is put into the focus of dialogue management. Hence, an approach for adapting the course of the dialogue to Interaction Quality, an objective variant of user satisfaction, is presented in this work. In general, rendering the...

We present a mechanism to learn repeated substructures of dialogue from an appropriately annotated dialogue corpus. We hypothesised that we could automatically segment a dialogue corpus into ‘chunks’ corresponding roughly to ‘game’-like structures. In our dialogue system we manage dialogue by employing a set of hand-crafted networks (ATNs) which we call ‘Dialogue Action Forms’ (DAFs). DAFs dete...

We present open dialogue management and its application to relational databases. An open dialogue manager generates dialogue states, actions, and default strategies from the semantics of its application domain. We define three open dialogue management tasks. First, vocabulary selection finds the intelligible attributes in each database table. Second, focus discovery selects candidate dialogue f...

Given the growing complexity of tasks that spoken dialogue systems are trying to handle, Reinforcement Learning (RL) has been increasingly used as a way of automatically learning the best policy for a system to make. While most work has focused on generating better policies for a dialogue manager, very little work has been done in using RL to construct a better dialogue state. This paper presen...

Spoken dialogue systems promise eecient and natural access to a large variety of information sources and services from any phone. However, current spoken dialogue systems are deecient in their strategies for preventing, identifying and repairing problems that arise in the conversation. This paper reports results on automatically training a Problematic Dialogue Predictor to predict problematic h...

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, n...

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...