Conversational AI (Record no. 85018)

000 -LEADER
fixed length control field 03961nam a22005295i 4500
001 - CONTROL NUMBER
control field 978-3-031-02176-3
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730163828.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220601s2021 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031021763
-- 978-3-031-02176-3
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
100 1# - AUTHOR NAME
Author McTear, Michael.
245 10 - TITLE STATEMENT
Title Conversational AI
Sub Title Dialogue Systems, Conversational Agents, and Chatbots /
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2021.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XVIII, 234 p.
490 1# - SERIES STATEMENT
Series statement Synthesis Lectures on Human Language Technologies,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Preface -- 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 ## - SUMMARY, ETC.
Summary, etc This 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.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-031-02176-3
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2021.
336 ## -
-- text
-- txt
-- rdacontent
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-- computer
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-- rdamedia
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-- online resource
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-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Natural language processing (Computer science).
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational linguistics.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Natural Language Processing (NLP).
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational Linguistics.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 1947-4059
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-- ZDB-2-SXSC

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