Ebook: Data-Driven Methods for Adaptive Spoken Dialogue Systems: Computational Learning for Conversational Interfaces

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.

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The EC FP7 project “Computational Learning in Adaptive Systems for Spoken Conversation” (CLASSiC) was a European initiative working on a fully data-driven architecture for the development of conversational interfaces, as well as new machine learning approaches for their sub-components. It developed a variety of novel statistical methods for spoken dialogue processing, for extended conversational interaction, which are now collected together in this book. A major focus of the project was in tracking the accumulation of information about user goals over multiple dialogue turns (i.e. extended conversational interaction), and in maintaining overall system robustness even when speech recognition results contain errors, by managing uncertainty through the processing chain.

Other advances were made in the areas of adaptive natural language generation (NLG), statistical methods for spoken language understanding (SLU), and machine learning methods for system optimisation, either during online operation, simulation, or from small amounts of data.

This book collects together the main research results and lessons learned in the CLASSiC project. Each chapter provides a summary of the specific methods developed and results obtained in its particular research area. In addition, leading researchers in statistical methods applied to industrial-scale dialogue systems (from SpeechCycle) have contributed a chapter surveying their recent work.

This volume will serve as a valuable introduction to the current state-of-the-art in statistical approaches to developing conversational interfaces, for active researchers in the field in industry and academia, as well as for students who are considering working in this exciting area.

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The EC FP7 project “Computational Learning in Adaptive Systems for Spoken Conversation” (CLASSiC) was a European initiative working on a fully data-driven architecture for the development of conversational interfaces, as well as new machine learning approaches for their sub-components. It developed a variety of novel statistical methods for spoken dialogue processing, for extended conversational interaction, which are now collected together in this book. A major focus of the project was in tracking the accumulation of information about user goals over multiple dialogue turns (i.e. extended conversational interaction), and in maintaining overall system robustness even when speech recognition results contain errors, by managing uncertainty through the processing chain.

Other advances were made in the areas of adaptive natural language generation (NLG), statistical methods for spoken language understanding (SLU), and machine learning methods for system optimisation, either during online operation, simulation, or from small amounts of data.

This book collects together the main research results and lessons learned in the CLASSiC project. Each chapter provides a summary of the specific methods developed and results obtained in its particular research area. In addition, leading researchers in statistical methods applied to industrial-scale dialogue systems (from SpeechCycle) have contributed a chapter surveying their recent work.

This volume will serve as a valuable introduction to the current state-of-the-art in statistical approaches to developing conversational interfaces, for active researchers in the field in industry and academia, as well as for students who are considering working in this exciting area.

Content:
Front Matter....Pages i-ix
Conversational Interfaces....Pages 1-4
Developing Dialogue Managers from Limited Amounts of Data....Pages 5-17
Data-Driven Methods for Spoken Language Understanding....Pages 19-38
User Simulation in the Development of Statistical Spoken Dialogue Systems....Pages 39-73
Optimisation for POMDP-Based Spoken Dialogue Systems....Pages 75-101
Statistical Approaches to Adaptive Natural Language Generation....Pages 103-130
Metrics and Evaluation of Spoken Dialogue Systems....Pages 131-150
Data-Driven Methods in Industrial Spoken Dialog Systems....Pages 151-171
Conclusion and Future Research Directions....Pages 173-177

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The EC FP7 project “Computational Learning in Adaptive Systems for Spoken Conversation” (CLASSiC) was a European initiative working on a fully data-driven architecture for the development of conversational interfaces, as well as new machine learning approaches for their sub-components. It developed a variety of novel statistical methods for spoken dialogue processing, for extended conversational interaction, which are now collected together in this book. A major focus of the project was in tracking the accumulation of information about user goals over multiple dialogue turns (i.e. extended conversational interaction), and in maintaining overall system robustness even when speech recognition results contain errors, by managing uncertainty through the processing chain.

Other advances were made in the areas of adaptive natural language generation (NLG), statistical methods for spoken language understanding (SLU), and machine learning methods for system optimisation, either during online operation, simulation, or from small amounts of data.

This book collects together the main research results and lessons learned in the CLASSiC project. Each chapter provides a summary of the specific methods developed and results obtained in its particular research area. In addition, leading researchers in statistical methods applied to industrial-scale dialogue systems (from SpeechCycle) have contributed a chapter surveying their recent work.

This volume will serve as a valuable introduction to the current state-of-the-art in statistical approaches to developing conversational interfaces, for active researchers in the field in industry and academia, as well as for students who are considering working in this exciting area.

Content:
Front Matter....Pages i-ix
Conversational Interfaces....Pages 1-4
Developing Dialogue Managers from Limited Amounts of Data....Pages 5-17
Data-Driven Methods for Spoken Language Understanding....Pages 19-38
User Simulation in the Development of Statistical Spoken Dialogue Systems....Pages 39-73
Optimisation for POMDP-Based Spoken Dialogue Systems....Pages 75-101
Statistical Approaches to Adaptive Natural Language Generation....Pages 103-130
Metrics and Evaluation of Spoken Dialogue Systems....Pages 131-150
Data-Driven Methods in Industrial Spoken Dialog Systems....Pages 151-171
Conclusion and Future Research Directions....Pages 173-177
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