4.1 Setting Utility Values.

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Preface; The IWSDS 2016 Conference; Dialogue State Tracking Challenge 4; Evaluation of Human-Robot Dialogue in Social Robotics; Sociocognitive Language Processing; Organization of IWSDS 2016; Steering Committee; Conference Chair; Local Arrangements Committee; Scientific Committee; Sponsors; Contents; Part I The Northernmost Spoken Dialogue Workshop; DigiSami and Digital Natives: Interaction Technology for the North Sami Language; 1 Introduction; 2 DigiSami and North Sami Language Resources; 2.1 The DigiSami Project; 2.2 The Sami Languages; 2.3 The North Sami Language.

2.1 Example-Based Dialog Managers and Their Evaluation2.2 Active Learning Framework; 3 Input Selection Strategies; 4 Experiments; 4.1 Experimental Setup; 4.2 Experimental Results and Additional Analysis; 5 Conclusion; References; Question Selection Based on Expected Utility to Acquire Information Through Dialogue; 1 Introduction; 2 Appropriate Questions for Acquiring Information; 3 Question Selection Based on Expected Utility; 3.1 Utility for Each Question Type; 3.2 Probability Representing Content of Questions; 3.3 Calculating Expected Utility; 4 Empirical Setting of Parameters.

2.4 Existing North Sami Language Resources2.5 Existing North Sami Speech Technology; 3 The DigiSami Corpus; 3.1 Preliminary Analysis: Engagement and Interaction; 3.2 Preliminary Analysis: Influence of Majority Language; 3.3 Preliminary Analysis: Adjectives in Spoken Language; 4 Towards SamiTalk: A Sami-Speaking Robot Application; 5 Conclusions and Future Work; References; Part II Methods and Techniques for Spoken Dialogue Systems; A Comparative Study of Text Preprocessing Techniques for Natural Language Call Routing; 1 Introduction; 2 Corpus Description; 3 Term Weighting Methods.

4 Dimensionality Reduction Methods5 Classification Algorithms; 6 Numerical Experiments; 7 Conclusions; References; Compact and Interpretable Dialogue State Representation with Genetic Sparse Distributed Memory; 1 Introduction; 2 Background; 2.1 The Reinforcement Learning Framework; 2.2 The Sparse Distributed Memory Model; 2.3 Genetic Sparse Distributed Memory for Classification; 3 Genetic Sparse Distributed Memory for Reinforcement Learning (GSDMRL); 3.1 Building the Set of Prototypes; 3.2 Q-Function Parametrisation; 3.3 Re-engineering the Prototypes.

4 Experiments with the NASTIA Dialogue System4.1 Comparing GSDMRL to a Grid-Based Representation; 4.2 Scalability and Interpretability of GSDMRL; 4.3 Results; 5 Conclusion; References; Incremental Human-Machine Dialogue Simulation; 1 Introduction; 2 Simulated Environment; 2.1 Service; 2.2 User Simulator; 2.3 ASR Output Simulator; 2.4 Scheduler; 3 Illustration; 3.1 Turn-Taking Strategy Example; 3.2 Evaluation; 4 Conclusion and Future Work; References; Active Learning for Example-Based Dialog Systems; 1 Introduction; 2 An Active Learning Framework for Example-Based Dialog Managers.

This book explores novel aspects of social robotics, spoken dialogue systems, human-robot interaction, spoken language understanding, multimodal communication, and system evaluation. It offers a variety of perspectives on and solutions to the most important questions about advanced techniques for social robots and chat systems. Chapters by leading researchers address key research and development topics in the field of spoken dialogue systems, focusing in particular on three special themes: dialogue state tracking, evaluation of human-robot dialogue in social robotics, and socio-cognitive language processing. The book offers a valuable resource for researchers and practitioners in both academia and industry whose work involves advanced interaction technology and who are seeking an up-to-date overview of the key topics. It also provides supplementary educational material for courses on state-of-the-art dialogue system technologies, social robotics, and related research fields.