Chapter 1. Conversational Interfaces.- Chapter 2. Developing Dialogue Managers from Limited Amounts of Data.- Chapter 3. Data-Driven Methods for Spoken Language Understanding.- Chapter 4. User Simulation in the Development of Statistical Spoken Dialogue Systems.- Chapter 5. Optimisation for POMDP-based Spoken Dialogue Systems.- Chapter 6. Statistical Approaches to Adaptive Natural Language Generation.- Chapter 7. Metrics and Evaluation of Spoken Dialogue Systems.- Chapter 8. Data-Driven Methods in Industrial Spoken Dialog Systems.- Chapter 9. Future Research Directions.
Data driven methods have long been used in Automatic Speech Recognition (ASR) and Text-To-Speech (TTS) synthesis and have more recently been introduced for dialogue management, spoken language understanding, and Natural Language Generation. Machine learning is now present "end-to-end" in Spoken Dialogue Systems (SDS). However, these techniques require data collection and annotation campaigns, which can be time-consuming and expensive, as well as dataset expansion by simulation. In this book, we provide an overview of the current state of the field and of recent advances, with a specific focus on adaptivity.
One of the first books to specifically address adaptive techniques used in dialogue system development
Practical examples developed by the editors and colleagues will be included
The book will be based on dialogue systems freely available for academic use