000			03846nam a22004815i 4500
001			u376847
003			SIRSI
005			20160812084433.0
007			cr nn 008mamaa
008			111122s2011 gw | s |||| 0|eng d
020			_a9783642249426 _9978-3-642-24942-6
040			_cMX-MeUAM
050		4	_aQA75.5-76.95
082	0	4	_a004 _223
100	1		_aRieser, Verena. _eauthor.
245	1	0	_aReinforcement Learning for Adaptive Dialogue Systems _h[recurso electrónico] : _bA Data-driven Methodology for Dialogue Management and Natural Language Generation / _cby Verena Rieser, Oliver Lemon.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011.
300			_aXVI, 256 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aTheory and Applications of Natural Language Processing
505	0		_a1.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.
520			_aThe 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 ongoing change. A new  methodology for developing spoken dialogue systems is described in detail. The journey starts and ends with human behaviour in interaction, and explores methods for learning from the data, for building simulation environments for training and testing systems, and for evaluating the results. The detailed material covers: Spoken and Multimodal dialogue systems, Wizard-of-Oz data collection, User Simulation methods, Reinforcement Learning, and Evaluation methodologies. The book is a research guide for students and researchers with a background in Computer Science, AI, or Machine Learning. It navigates through a detailed case study in data-driven methods for development and evaluation of spoken dialogue systems. Common challenges associated with this approach are discussed and example solutions are provided. This work provides insights, lessons, and inspiration for future research and development – not only for spoken dialogue systems in particular, but for data-driven approaches to human-machine interaction in general.
650		0	_aComputer science.
650		0	_aArtificial intelligence.
650		0	_aTranslators (Computer programs).
650	1	4	_aComputer Science.
650	2	4	_aComputer Science, general.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aLanguage Translation and Linguistics.
650	2	4	_aUser Interfaces and Human Computer Interaction.
700	1		_aLemon, Oliver. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642249419
830		0	_aTheory and Applications of Natural Language Processing
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-24942-6
596			_a19
942			_cLIBRO_ELEC
999			_c204727 _d204727