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Reinforcement Learning for Adaptive Dialogue Systems A Data-driven Methodology for Dialogue Management and Natural Language Generation /
The past decade has seen a revolution in the field of spoken dialogue systems. As in other areas of Computer Science and Artificial Intelligence, data-driven methods are now being used to drive new methodologies for system development and evaluation. This book is a unique contribution to that ongoin...
| Clasificación: | Libro Electrónico |
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| Autores principales: | , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2011.
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| Edición: | 1st ed. 2011. |
| Colección: | Theory and Applications of Natural Language Processing,
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| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- 1.Introduction
- 2.Background
- 3.Reinforcement Learning for Information Seeking dialogue strategies
- 4.The bootstrapping approach to developing Reinforcement Learning-based strategies
- 5.Data Collection in aWizard-of-Oz experiment
- 6.Building a simulated learning environment from Wizard-of-Oz data
- 7.Comparing Reinforcement and Supervised Learning of dialogue policies with real users
- 8.Meta-evaluation
- 9.Adaptive Natural Language Generation
- 10.Conclusion
- References
- Example Dialogues
- A.1.Wizard-of-Oz Example Dialogues
- A.2.Example Dialogues from Simulated Interaction
- A.3.Example Dialogues from User Testing
- Learned State-Action Mappings
- Index.