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008			220601s2021 sz | s |||| 0|eng d
020			_a9783031021763
024	7		_a10.1007/978-3-031-02176-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.H85 _b2021 EB
100			_aMcTear, Michael _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _999321
245	1	0	_aConversational AI : _bDialogue Systems, Conversational Agents, and Chatbots _cby Michael McTear
250			_a1st edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XVIII, 234 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Human Language Technologies _x1947-4059
505	0		_aPreface -- Acknowledgments -- Glossary -- Introducing Dialogue Systems -- Rule-Based Dialogue Systems: Architecture, Methods, and Tools -- Statistical Data-Driven Dialogue Systems -- Evaluating Dialogue Systems -- End-to-End Neural Dialogue Systems -- Challenges and Future Directions -- Bibliography -- Author's Biography .
520			_aThis book provides a comprehensive introduction to Conversational AI. While the idea of interacting with a computer using voice or text goes back a long way, it is only in recent years that this idea has become a reality with the emergence of digital personal assistants, smart speakers, and chatbots. Advances in AI, particularly in deep learning, along with the availability of massive computing power and vast amounts of data, have led to a new generation of dialogue systems and conversational interfaces. Current research in Conversational AI focuses mainly on the application of machine learning and statistical data-driven approaches to the development of dialogue systems. However, it is important to be aware of previous achievements in dialogue technology and to consider to what extent they might be relevant to current research and development. Three main approaches to the development of dialogue systems are reviewed: rule-based systems that are handcrafted using best practice guidelines; statistical data-driven systems based on machine learning; and neural dialogue systems based on end-to-end learning. Evaluating the performance and usability of dialogue systems has become an important topic in its own right, and a variety of evaluation metrics and frameworks are described. Finally, a number of challenges for future research are considered, including: multimodality in dialogue systems, visual dialogue; data efficient dialogue model learning; using knowledge graphs; discourse and dialogue phenomena; hybrid approaches to dialogue systems development; dialogue with social robots and in the Internet of Things; and social and ethical issues.
988			_aSynthesis Collection of Technology_2021
650		7	_2embne _9158738 _aProceso en lenguaje natural (Informática)
650		7	_2embne _9155848 _aInteracción hombre-ordenador
650		7	_2embne _aInteligencia artificial _9413115
776	0	8	_iPrinted edition: _z9783031001871
776	0	8	_iPrinted edition: _z9783031010484
776	0	8	_iPrinted edition: _z9783031033049
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02176-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI