Titlebook: Dialogues with Social Robots; Enablements, Analyse Kristiina Jokinen,Graham Wilcock Book 2017 Springer Science+Business Media Singapore 201

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PLE

Compact and Interpretable Dialogue State Representation with Genetic Sparse Distributed Memory for classification is adapted to the Reinforcement Learning task and a new way of building the prototypes is proposed. The approach is tested on a corpus of dialogues collected with an appointment scheduling system. The results are compared to a grid-based linear parametrisation. It is shown that l

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How to Study a D.H. Lawrence Novel for classification is adapted to the Reinforcement Learning task and a new way of building the prototypes is proposed. The approach is tested on a corpus of dialogues collected with an appointment scheduling system. The results are compared to a grid-based linear parametrisation. It is shown that l

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Compact and Interpretable Dialogue State Representation with Genetic Sparse Distributed Memoryialogue Systems (SDS) trained by Reinforcement Learning (RL) is often designed to reflect user satisfaction. To do so, the state space representation should be based on features capturing user satisfaction characteristics such as the mean speech recognition confidence score for instance. On the othe

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Incremental Human-Machine Dialogue Simulation and test data-driven methods. We review the various simulator components in detail, including an unstable speech recognizer, and their differences with non-incremental approaches. Then, as an illustration of its capacities, an incremental strategy based on hand-crafted rules is implemented and comp