Conversational AI Dialogue Systems Conversational Agents and Chatbots

251 Pages·2020·9.297 MB·English

by  Michael F. McTear

Conversational AI
Dialogue Systems, Conversational Agents,
and Chatbots

Synthesis Lectures on Human
Language Technologies
Editor
GraemeHirst,UniversityofToronto
SynthesisLecturesonHumanLanguageTechnologiesiseditedbyGraemeHirstoftheUniversity
ofToronto.Theseriesconsistsof50-to150-pagemonographsontopicsrelatingtonatural
languageprocessing,computationallinguistics,informationretrieval,andspokenlanguage
understanding.Emphasisisonimportantnewtechniques,onnewapplications,andontopicsthat
combinetwoormoreHLTsubfields.
ConversationalAI:DialogueSystems,ConversationalAgents,andChatbots
MichaelMcTear
2020
EmbeddingsinNaturalLanguageProcessing:TheoryandAdvancesinVector
RepresentationsofMeaning
MohammadTaherPilehvarandJoseCamacho-Collados
2020
NaturalLanguageProcessingforSocialMedia,ThirdEdition
AnnaAtefehFarzindarandDianaInkpen
2020
StatisticalSignificanceTestingforNaturalLanguageProcessing
RotemDror,LotemPeled,SegevShlomov,andRoiReichart
2020
DeepLearningApproachestoTextProduction
ShashiNarayanandClaireGardent
2020
LinguisticFundamentalsforNaturalLanguageProcessingII:100Essentialsfrom
SemanticsandPragmatics
EmilyM.BenderandAlexLascarides
2019
iv
Cross-LingualWordEmbeddings
AndersSøgaard,IvanVulić,SebastianRuder,ManaalFaruqui
2019
BayesianAnalysisinNaturalLanguageProcessing,SecondEdition
ShayCohen
2019
ArgumentationMining
ManfredStedeandJodiSchneider
2018
QualityEstimationforMachineTranslation
LuciaSpecia,CarolinaScarton,andGustavoHenriquePaetzold
2018
NaturalLanguageProcessingforSocialMedia,SecondEdition
AtefehFarzindarandDianaInkpen
2017
AutomaticTextSimplification
HoracioSaggion
2017
NeuralNetworkMethodsforNaturalLanguageProcessing
YoavGoldberg
2017
Syntax-basedStatisticalMachineTranslation
PhilipWilliams,RicoSennrich,MattPost,andPhilippKoehn
2016
Domain-SensitiveTemporalTagging
JannikStrötgenandMichaelGertz
2016
LinkedLexicalKnowledgeBases:FoundationsandApplications
IrynaGurevych,JudithEckle-Kohler,andMichaelMatuschek
2016
BayesianAnalysisinNaturalLanguageProcessing
ShayCohen
2016
Metaphor:AComputationalPerspective
TonyVeale,EkaterinaShutova,andBeataBeigmanKlebanov
2016
v
GrammaticalInferenceforComputationalLinguistics
JeffreyHeinz,ColindelaHiguera,andMennovanZaanen
2015
AutomaticDetectionofVerbalDeception
EileenFitzpatrick,JoanBachenko,andTommasoFornaciari
2015
NaturalLanguageProcessingforSocialMedia
AtefehFarzindarandDianaInkpen
2015
SemanticSimilarityfromNaturalLanguageandOntologyAnalysis
SébastienHarispe,SylvieRanwez,StefanJanaqi,andJackyMontmain
2015
LearningtoRankforInformationRetrievalandNaturalLanguageProcessing,Second
Edition
HangLi
2014
Ontology-BasedInterpretationofNaturalLanguage
PhilippCimiano,ChristinaUnger,andJohnMcCrae
2014
AutomatedGrammaticalErrorDetectionforLanguageLearners,SecondEdition
ClaudiaLeacock,MartinChodorow,MichaelGamon,andJoelTetreault
2014
WebCorpusConstruction
RolandSchäferandFelixBildhauer
2013
RecognizingTextualEntailment:ModelsandApplications
IdoDagan,DanRoth,MarkSammons,andFabioMassimoZanzotto
2013
LinguisticFundamentalsforNaturalLanguageProcessing:100Essentialsfrom
MorphologyandSyntax
EmilyM.Bender
2013
Semi-SupervisedLearningandDomainAdaptationinNaturalLanguageProcessing
AndersSøgaard
2013
vi
SemanticRelationsBetweenNominals
ViviNastase,PreslavNakov,DiarmuidÓSéaghdha,andStanSzpakowicz
2013
ComputationalModelingofNarrative
InderjeetMani
2012
NaturalLanguageProcessingforHistoricalTexts
MichaelPiotrowski
2012
SentimentAnalysisandOpinionMining
BingLiu
2012
DiscourseProcessing
ManfredStede
2011
BitextAlignment
JörgTiedemann
2011
LinguisticStructurePrediction
NoahA.Smith
2011
LearningtoRankforInformationRetrievalandNaturalLanguageProcessing
HangLi
2011
ComputationalModelingofHumanLanguageAcquisition
AfraAlishahi
2010
IntroductiontoArabicNaturalLanguageProcessing
NizarY.Habash
2010
Cross-LanguageInformationRetrieval
Jian-YunNie
2010
AutomatedGrammaticalErrorDetectionforLanguageLearners
ClaudiaLeacock,MartinChodorow,MichaelGamon,andJoelTetreault
2010
vii
Data-IntensiveTextProcessingwithMapReduce
JimmyLinandChrisDyer
2010
SemanticRoleLabeling
MarthaPalmer,DanielGildea,andNianwenXue
2010
SpokenDialogueSystems
KristiinaJokinenandMichaelMcTear
2009
IntroductiontoChineseNaturalLanguageProcessing
Kam-FaiWong,WenjieLi,RuifengXu,andZheng-shengZhang
2009
IntroductiontoLinguisticAnnotationandTextAnalytics
GrahamWilcock
2009
DependencyParsing
SandraKübler,RyanMcDonald,andJoakimNivre
2009
StatisticalLanguageModelsforInformationRetrieval
ChengXiangZhai
2008
Copyright©2021byMorgan&Claypool
Allrightsreserved.Nopartofthispublicationmaybereproduced,storedinaretrievalsystem,ortransmittedin
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ConversationalAI:DialogueSystems,ConversationalAgents,andChatbots
MichaelMcTear
www.morganclaypool.com
ISBN:9781636390314 paperback
ISBN:9781636390321 ebook
ISBN:9781636390338 hardcover
DOI10.2200/S01060ED1V01Y202010HLT048
APublicationintheMorgan&ClaypoolPublishersseries
SYNTHESISLECTURESONHUMANLANGUAGETECHNOLOGIES
Lecture#48
SeriesEditor:GraemeHirst,UniversityofToronto
SeriesISSN
Print1947-4040 Electronic1947-4059
Conversational AI
Dialogue Systems, Conversational Agents,
and Chatbots
Michael McTear
UlsterUniversity
SYNTHESISLECTURESONHUMANLANGUAGETECHNOLOGIES#48
M
&C Morgan &cLaypool publishers
ABSTRACT
This book provides a comprehensive introduction to Conversational AI. While the idea of in-
teractingwithacomputerusingvoiceortextgoesbackalongway,itisonlyinrecentyearsthat
this idea has become a reality with the emergence of digital personal assistants, smart speak-
ers, 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 develop-
ment 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
anddevelopment.Threemainapproachestothedevelopmentofdialoguesystemsarereviewed:
rule-based systems that are handcrafted using best practice guidelines; statistical data-driven
systemsbasedonmachinelearning;andneuraldialoguesystemsbasedonend-to-endlearning.
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; dis-
courseanddialoguephenomena;hybridapproachestodialoguesystemsdevelopment;dialogue
withsocialrobotsandintheInternetofThings;andsocialandethicalissues.
KEYWORDS
conversationalinterface,dialoguesystem,voiceuserinterface,embodiedconversa-
tional agent, chatbot, deep learning, data-driven, statistical, end-to-end learning,
evaluation metrics, performance evaluation, usability, multimodality, hybrid sys-
tems,ethicalissues