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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.
2.4 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.
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.
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.