This paper investigates the impact of reward shaping on a reinforcement learning-based spoken dialogue system’s learning. A diffuse reward function gives a reward after each transition between two dialogue states. A sparse function only gives a reward at the end of the dialogue. Reward shaping consists of learning a diffuse function without modifying the optimal policy compared to a sparse one....

Within the broad field of spoken dialogue systems, the application of machine-learning approaches to dialogue management strategy design is a rapidly growing research area. The main motivation is the hope of building systems that learn through trial-and-error interaction what constitutes a good dialogue strategy. Training of such systems could in theory be done using human users or using corpor...

Interactive learning environments have been built to support various audiences from preschool to university students. However, it is not yet known how to bring the great promise of tutorial dialogue systems, which engage students in rich natural language, to bear for young learners such as those in grades K-5. This doctoral consortium paper presents our goal of developing a dialogue system in t...

Spoken Dialogue Systems (SDS) are man-machine interfaces which use natural language as the medium of interaction. Dialogue corpora collection for the purpose of training and evaluating dialogue systems is an expensive process. User simulators aim at simulating human users in order to generate synthetic data. Existing methods for user simulation mainly focus on generating data with the same stat...

This paper shows how the convergence between design and monitoring tools, and the integration of a dedicated reinforcement learning can be complementary and offer a new design experience for Spoken Dialogue System (SDS) developers. Most industrial SDS developers use a graphical tool to implement the dialogue strategies. This article proposes first to integrate dialogue logs into this design too...

This paper addresses the issue of on-line detection of communication problems in spoken dialogue systems. The usefulness is investigated of the sequence of system question types and the word graphs corresponding to the respective user utterances. By applying both rule-induction and memory-based learning techniques to data obtained with a Dutch train time-table information system, the current pa...

From a system developer’s perspective, designing a spoken dialogue system can be a time-consuming and difficult process. A developer may spend a lot of time anticipating how a potential user might interact with the system and then deciding on the most appropriate system response. These decisions are encoded in a dialogue strategy, essentially a mapping between anticipated user inputs and approp...

Quality of Telephone-Based Spoken Dialogue Systems. Quality of Human-Machine Interaction over the Phone.Quality prediction models for telephone-based spoken dialogue systems. Perform to guarantee an acceptable overall quality for the user. In order to.action HHI as one reference for telephonebased humanmachine interaction HMI. The quality of interactions with spoken dialogue systems is difficul...