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001 978-3-031-02176-3
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020 _a9783031021763
_9978-3-031-02176-3
024 7 _a10.1007/978-3-031-02176-3
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aMcTear, Michael.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980704
245 1 0 _aConversational AI
_h[electronic resource] :
_bDialogue Systems, Conversational Agents, and Chatbots /
_cby Michael McTear.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXVIII, 234 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aArtificial intelligence.
_93407
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aComputational linguistics.
_96146
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aComputational Linguistics.
_96146
710 2 _aSpringerLink (Online service)
_980705
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031001871
776 0 8 _iPrinted edition:
_z9783031010484
776 0 8 _iPrinted edition:
_z9783031033049
830 0 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
_980706
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02176-3
912 _aZDB-2-SXSC
942 _cEBK
999 _c85018
_d85018