Conversational AI Dialogue Systems Conversational Agents and Chatbots PDF
Preview Conversational AI Dialogue Systems Conversational Agents and Chatbots
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 anyformorbyanymeans—electronic,mechanical,photocopy,recording,oranyotherexceptforbriefquotations inprintedreviews,withoutthepriorpermissionofthepublisher. 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
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