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[46]提出了意图识别与槽位填充双向交互的Transormer,通过两个任务间建立双向连接来考虑他们间的交叉影响。显示联合建模相较隐式联合建模有一定优势,可以使模型完全捕获跨任务的共享知识,从而提高两个任务的性能。其次,明确控制两项任务的知识转移可以帮助提高可解释性,从而可以轻松分析槽位填充任务和意图识别任务之间的影响。参考文献TuringAM.Computingmachineryandintelligence[M].Parsingtheturingtest.Springer,Dordrecht,2009:23-65.WeizenbaumJ.ELIZA—acomputerprogramforthestudyofnaturallanguagecommunicationbetweenmanandmachine[J].CommunicationsoftheACM,1966,9(1):36-45.赵阳洋,王振宇,王佩等.任务型对话系统研究综述[J].计算机学报,2020,43(10):1862–1896.StallardD,BobrowR.FragmentprocessingintheDELPHIsystem[C].SpeechandNaturalLanguage:ProceedingsofaWorkshopHeldatHarriman,NewYork,February23-26,1992.1992.DingR,XieP,ZhangX,etal.Aneuralmulti-digraphmodelforChineseNERwithgazetteers[C].Proceedingsofthe57thAnnualMeetingoftheAssociationforComputationalLinguistics.2019:1462-1467.GuiT,ZouY,ZhangQ,etal.Alexicon-basedgraphneuralnetworkforchinesener[C].Proceedingsofthe2019ConferenceonEmpiricalMethodsinNaturalLanguageProcessingandthe9thInternationalJointConferenceonNaturalLanguageProcessing(EMNLP-IJCNLP).2019:1039-1049.SuiD,ChenY,LiuK,etal.LeveragelexicalknowledgeforChinesenamedentityrecognitionviacollaborativegraphnetwork[C].Proceedingsofthe2019ConferenceonEmpiricalMethodsinNaturalLanguageProcessingandthe9thInternationalJointConferenceonNaturalLanguageProcessing(EMNLP-IJCNLP).2019:3821-3831.LiuY,MengF,ZhangJ,etal.CM-Net:ANovelCollaborativeMemoryNetworkforSpokenLanguageUnderstanding[C].Proceedingsofthe2019ConferenceonEmpiricalMethodsinNaturalLanguageProcessingandthe9thInternationalJointConferenceonNaturalLanguageProcessing(EMNLP-IJCNLP).2019:1050-1059.SutskeverI,VinyalsO,LeQV.Sequencetosequencelearningwithneuralnetworks[J].arXivpreprintarXiv:1409.3215,2014.WenTH,VandykeD,MrkšićN,etal.Anetwork-basedend-to-endtrainabletask-orienteddialoguesystem[C].Proceedingsofthe15thConferenceoftheEuropeanChapteroftheAssociationforComputationalLinguistics.2017:438-449.LiX,ChenYN,LiL,etal.End-to-EndTask-CompletionNeuralDialogueSystems[C].ProceedingsoftheEighthInternationalJointConferenceonNaturalLanguageProcessing(Volume1:LongPapers).2017:733-743.DowdingJ,GawronJM,AppeltD,etal.GEMINI:ANATURALLANGUAGESYSTEMFORSPOKEN-LANGUAGEUNDERSTANDING[J].Syntax,94:90.9.LiX,RothD.Learningquestionclassifiers:theroleofsemanticinformation[J].NaturalLanguageEngineering,2006,12(3):229-250.PragerJ,RadevD,BrownE,etal.TheuseofpredictiveannotationforquestionansweringinTREC8[C].InNISTSpecialPublication500-246:TheEighthTextREtrievalConference(TREC8.1999.RamanandJ,BhavsarK,PedanekarN.Wishfulthinking-findingsuggestionsand’buy’wishesfromproductreviews[C].ProceedingsoftheNAACLHLT2010workshoponcomputationalapproachestoanalysisandgenerationofemotionintext.2010:54-61.GenkinA,LewisDD,MadiganD.Large-scaleBayesianlogisticregressionfortextcategorization[J].technometrics,2007,49(3):291-304.HaffnerP,TurG,WrightJH.OptimizingSVMsforcomplexcallclassification[C].2003IEEEInternationalConferenceonAcoustics,Speech,andSignalProcessing,2003.Proceedings.(ICASSP'03).IEEE,2003,1:I-I.McCallumA,NigamK.Acomparisonofeventmodelsfornaivebayestextclassification[C].AAAI-98workshoponlearningfortextcategorization.1998,752(1):41-48.MikolovT,ChenK,CorradoG,etal.Efficientestimationofwordrepresentationsinvectorspace[J].1stInternationalConferenceonLearningRepresentations,2013.陈浩辰.基于微博的消费意图挖掘[D].哈尔滨:哈尔滨工业大学,2014.RakhlinA.ConvolutionalNeuralNetworksforSentenceClassification[J].Proceedingsofthe2014ConferenceonEmpiricalMethodsinNaturalLanguageProcessing(EMNLP),2014:1746--1751.RavuriS,StolckeA.RecurrentneuralnetworkandLSTMmodelsforlexicalutteranceclassification[C].SixteenthAnnualConferenceoftheInternationalSpeechCommunic

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