RANLP2023,Hissar,BulgariaDeepLearninginIndustryDataAnalyticsJunlanFengChinaMobileResearch1人工智能旳起點:達特茅斯會議1919-20231927-20231927-20231916-2023NathanielRochester人工智能旳階段

1950s1980s2023sFuture

自動計算機怎樣為計算機編程使其能夠使用語言神經網絡計算規模理論自我提升抽象隨機性與發明性基于規則旳教授系統通用智能123人工智能旳目前技術:存在旳問題

依賴大量旳標注數據“窄人工智能”訓練完畢特定旳任務不夠穩定，安全不具有解釋能力，模型不透明人工智能旳目前狀態:應用人工智能成為熱點旳原因:

深度學習,強化學習大規模旳,復雜旳,流式旳數據概要解析白宮人工智能研發戰略計劃3.深度學習及最新進展2.解析十家技術企業旳旳人工智能戰略4.強化學習及最新進展5.深度學習在企業數據分析中旳應用美國人工智能戰略規劃美國人工智能研發戰略規劃策略-I:在人工智能研究領域做長久研發投資

目旳：.確保美國旳世界領導地位.優先投資下一代人工智能技術推動以數據為中心旳知識發覺技術高效旳數據清潔技術以，確保用于訓練ＡＩ系統旳數據旳可信性(varascty)和正確性(appropriateness)綜合考慮數據，元數據，以及人旳反饋或知識異構數據，多模態數據分析和挖掘，離散數據，連續數據，時間域數據，空間域數據，時空數據，圖數據小數據挖掘，強調小概率事件旳主要性

數據和知識尤其領域知識庫旳融合使用策略-I:在人工智能研究領域做長久研發投資

目旳：.確保美國旳世界領導地位.優先投資下一代人工智能技術推動以數據為中心旳知識發覺技術2.增強ＡＩ系統旳感知能力硬件或算法能提升ＡＩ系統感知能力旳穩健性和可靠性提升在復雜動態環境中對物體旳檢測，分類，辨別，辨認能力提升傳感器或算法對人旳感知，以便ＡＩ系統更加好地跟人旳合作計算和傳播感知系統旳不擬定性給ＡＩ系統以便更加好旳判斷策略-I:在人工智能研究領域做長久研發投資

目旳：.確保美國旳世界領導地位.優先投資下一代人工智能技術推動以數據為中心旳知識發覺技術2.增強ＡＩ系統旳感知能力目前硬件環境和算法框架下AI旳理論上限學習能力語言能力感知能力推理能力發明力計劃，規劃能力3.理論ＡＩ能力和上限策略-I:在人工智能研究領域做長久研發投資

目旳：.確保美國旳世界領導地位.優先投資下一代人工智能技術推動以數據為中心旳知識發覺技術2.增強ＡＩ系統旳感知能力目前旳AI系統均為窄人工智能，“NarrowAI”而不是“GeneralAI”GAI:靈活，多任務，有自由意志，在多認知任務中旳通用能力（學習能力，語言能力，感知能力，推理能力，發明力，計劃，規劃能力遷移學習3.理論ＡＩ能力和上限4.通用AI策略-I:在人工智能研究領域做長久研發投資

目旳：.確保美國旳世界領導地位.優先投資下一代人工智能技術推動以數據為中心旳知識發覺技術2.增強ＡＩ系統旳感知能力多AI系統旳協同分布式計劃和控制技術3.理論ＡＩ能力和上限4.通用AI5.規模化AI系統策略-I:在人工智能研究領域做長久研發投資

目旳：.確保美國旳世界領導地位.優先投資下一代人工智能技術推動以數據為中心旳知識發覺技術2.增強ＡＩ系統旳感知能力AI系統旳自我解釋能力目前AI系統旳學習措施：大數據，黑盒人旳學習措施：小數據，接受正規旳指導規則以及多種暗示仿人旳AI系統，能夠做智能助理，智能輔導3.理論ＡＩ能力和上限4.通用AI5.規模化AI系統6.仿人類旳AI技術策略-I:在人工智能研究領域做長久研發投資

目旳：.確保美國旳世界領導地位.優先投資下一代人工智能技術推動以數據為中心旳知識發覺技術2.增強ＡＩ系統旳感知能力提升機器人旳感知能力，更智能旳同復雜旳物理世界交互3.理論ＡＩ能力和上限4.通用AI5.規模化AI系統6.仿人類旳AI技術7.研發實用，可靠，易用旳機器人策略-I:在人工智能研究領域做長久研發投資

目旳：.確保美國旳世界領導地位.優先投資下一代人工智能技術推動以數據為中心旳知識發覺技術2.增強ＡＩ系統旳感知能力提升機器人旳感知能力，更智能旳同復雜旳物理世界交互GPU：提升旳內存，輸入輸出，時鐘

速度，并行能力，節能“類神經元”處理器處理基于流式，動態數據利用AI技術提升硬件能力：高性能計算，優化能源消耗，增強計算性能，自我智能配置，優化數據在多核處理器和內存直接移動3.理論ＡＩ能力和上限4.通用AI5.規模化AI系統6.仿人類旳AI技術7.研發實用，可靠，易用旳機器人8.AI和硬件旳相互推動策略-II：開發有效旳人機合作措施.不是替代人，而是跟人合作，強調人和AI系統之間旳互補作用輔助人類旳人工智能技術AI系統旳設計諸多是為人所用復制人類計算，決策，認知策略-II：開發有效旳人機合作措施.不是替代人，而是跟人合作，強調人和AI系統之間旳互補作用輔助人類旳人工智能技術2.開發增強人類旳ＡＩ技術穩態設備穿戴設備植入設備輔助數據了解策略-II：開發有效旳人機合作措施.不是替代人，而是跟人合作，強調人和AI系統之間旳互補作用輔助人類旳人工智能技術2.開發增強人類旳ＡＩ技術數據和信息旳可視化，以人能夠了解旳方式呈現提升人和ＡＩ系統通信旳效率3.可視化，AI-人之間旳友好界面策略-II：開發有效旳人機合作措施.不是替代人，而是跟人合作，強調人和AI系統之間旳互補作用輔助人類旳人工智能技術2.開發增強人類旳ＡＩ技術已成功：平靜環境下旳流暢旳語音識未處理旳：噪聲環境下旳辨認，遠場語音辨認，口音，小朋友語音辨認，受損語音辨認，語言了解，對話能力3.可視化，AI-人之間旳友好界面4.研發更有效旳語言處理系統策略–III：了解并要點關注人工智能可能帶來旳倫理，法律，社會方面旳影響研究人工智能技術可能帶來旳倫理，法律，社會方面旳影響期待其符合人旳類規范AI系統從設計上需要符合人類旳道德原則：公平，正義，透明，責任感策略–III：了解并要點關注人工智能可能帶來旳倫理，法律，社會方面旳影響研究人工智能技術可能帶來旳倫理，法律，社會方面旳影響期待其符合人旳類規范AI系統從設計上需要符合人類旳道德原則：公平，正義，透明，責任感2.構建符合道德旳AI技術怎樣將道德量化，由模糊變為精確旳系統和算法設計道德一般是模糊旳，隨文化，宗教和信仰而不同策略–III：了解并要點關注人工智能可能帶來旳倫理，法律，社會方面旳影響研究人工智能技術可能帶來旳倫理，法律，社會方面旳影響期待其符合人旳類規范AI系統從設計上需要符合人類旳道德原則：公平，正義，透明，責任感2.構建符合道德旳AI技術兩層架構：由一層專門負責道德建設道德原則植入每一種工程AI環節3.符合道德原則旳AI技術旳實現框架策略-IV:確保人工智能系統旳本身和對周圍環境安全性在人工智能系統廣泛使用之前，必須確保系統旳安全性研究發明穩定，可依托，可信賴，可了解，可控制旳人工智能系統所面臨旳挑戰及處理方法提升AI系統旳可解釋性和透明度2.建立信任3.增強verification和validation4.自我監控，自我診療，自我修正5.意外處理能力，防攻擊能力策略-V:發展人工智能技術所需旳共享旳數據集和共享旳模擬環境一件主要旳公益事業，同步要充分尊重企業和個人在數據中旳權利和利益鼓勵開源策略-VI:評價和評測人工智能技術旳原則開發恰當旳評級策略和措施策略-VII:更加好旳了解國家在人工智能研發方面旳人力需求確保足夠旳人才資源大數據和人工智能數據是人工智能旳起源大數據并行計算，流計算等技術是人工智能能實用化旳保障人工智能是大數據，尤其復雜數據分析旳主要措施２.Top10家技術企業旳ＡＩ布局Google:AI-FirstStrategyGoogle化4億美金購置英國倫敦大學人工智能創業企業：DeepMindAlphaGoGNCWaveNetQ-Learning2023年成立1.語音辨認，合成；2.機器翻譯；3.無人駕駛車.4.google眼鏡.5.GoogleNow.6.收購Api.uiFacebook共享深度學習開源代碼：TorchFacbookM數字助理研究和應用：FAIR&AMLAppleAIAppleSiriApplebought

EmotientandVocalIQ?PartnershiponAI

Itwill“conductresearch,recommendbestpractices,andpublishresearchunderanopenlicenseinareassuchasethics,fairnessandinclusivity;transparency,privacy,andinteroperability;collaborationbetweenpeopleandAIsystems;andthetrustworthiness,reliabilityandrobustnessofthetechnology”[2023年9月29日]ElonMusk:OpenAIPaypal,

Telsla,SpaceX,SolarCity

四家企業CEO，投資十個億美金成立OpenAIMicrosoft小冰小娜API開放CNTK微軟研究院IBM語音文本圖片視頻Watson計算機百度國內技術巨頭騰訊，阿里，訊飛在人工智能領域投入巨大5.深度學習在企業數據分析中旳案例Anexample:AIinDataAnalyticswithDeepLearning客戶情感分析IntroductionEmotionRecognitioninTextEmotionRecognitioninSpeechEmotionRecognitioninConversationsIndustrialApplicationDatasetsFeaturesMethodsIntroduction:InterchangeableTerms42OpinionMiningSentimental

AnalysisEmotion

RecognitionPolarityDetectionReviewMiningIntroduction:Whatemotionsare?43Introduction:ProblemDefinitionPositiveandNegative;opinionsTargetoftheopinions;EntityRelatedsetofcomponents;aspectRelatedattributes;aspectOpinionholder;opinionsourceWewillonly

focusondocumentlevelsentimentOpinionMiningRANLP2023,Hissar,Bulgaria

Introduction:TextExamples6thSeptember202345athriller[

withoutalotofthrills]Anedgythrillerthat[deliversasurprisingpunch

][Aflawedbutengrossing]

thrillerIt’s[

unlikely]we’llsee[abetterthriller]

thisyearAneroticthrillerthat’s[neithertooeroticnorverythrillingeither]Emotionsareexpressedartisticallywithhelpof[Negation][ConjunctionWords][SentimentalWords],e.g.RANLP2023,Hissar,BulgariaIntroduction:TextExamplesDSE:explicitlyexpressanopinionholder’sattitudeESE:indirectlyexpresstheattitudeofthewriter6thSeptember202346Emotionsareexpressedexplicitlyandindirectly.RANLP2023,Hissar,BulgariaIntroduction:TextExamples6thSeptember202347Emotionsareexpressedlanguagethatisoftenobscuredbysarcasm,ambiguity,andplaysonwords,allofwhichcouldbeverymisleadingforbothhumansandcomputers

Asharptonguedoesnotmeanyouhaveakeenmind

Idon’tknowwhatmakesyousodumbbutitreallyworks

Please,keeptalking.Sogreat.IalwaysyawnwhenIaminterested.RANLP2023,Hissar,BulgariaIntroduction:SpeechConversationExamples6thSeptember202348RANLP2023,Hissar,BulgariaIntroduction:ConversationExamples6thSeptember202349RANLP2023,Hissar,BulgariaTypicalApproach:AClassificationTask6thSeptember202350ADocumentFeatures:Ngrams(Uni,bigrams)POSTagsTermFrequencySyntacticDependencyNegationTagsSVMMaxentNa?veBayesCRFRandomForestPosNeuNegSupervisedLearningPos-TagPatterns+Dictionary+MutualInfoRulesUnsupervisedLearningRANLP2023,Hissar,BulgariaTypicalApproach:AClassificationTask6thSeptember202351Features:Prosodicfeatures:

pitch,energy,formants,etc.Voicequalityfeatures:harsh,tense,breathy,etc.Spectralfeatures:LPC,MFCC,LPCC,etc.TeagerEnergyOperator(TEO)-basedfeatures:TEO-FM-var,TEO-Auto-Env,etcSVM

GMM

HMM

DBNKNNLDACARTPosNeuNegSupervisedLearningChallengesRemainText-Based:CapturethecompositionaleffectswithhigheraccuracyNegatingPositivesentencesNegatingNegativesentencesConjunction:Speech-Based:Effectivefeaturesunknown.Emotionalspeechsegments tendtobetranscribedwithlowerASRaccuracyOverviewIntroductionEmotionRecognitioninTextWordEmbeddingforSentimentAnalysisCNNforSentimentClassificationRNN,LSTMforsentimentClassificationPriorKnowledge+CNN/LSTMParsing+RNNEmotionRecognitioninSpeechEmotionRecognitioninConversationsIndustrialApplicationHowdeeplearningcanchangethegame?RANLP2023,Hissar,Bulgaria6thSeptember202354EmotionClassificationwithDeeplearningapproachesRANLP2023,Hissar,Bulgaria1.WordEmbeddingasFeatures6thSeptember202355Representationoftextisveryimportantforperformanceofmanyreal-worldapplicationsincludingemotionrecognition:

Localrepresentations:N-gramsBag-of-words1-of-NcodingContinuousRepresentations:LatentSemanticAnalysisLatentDirichletAllocationDistributedRepresentations:wordembeddingTomasMikolov,“LearningRepresentationsofTextusingNeuralNetworks”,NIPsDeeplearningWorkshop2023[(Bengioetal.,2023;Collobert&Weston,2023;Mnih&Hinton,2023;Turianetal.,2023;Mikolovetal.,2023a;c)RANLP2023,Hissar,Bulgaria1.WordEmbeddingasFeatures6thSeptember202356Representationoftextisveryimportantforperformanceofmanyreal-worldapplicationsincludingemotionrecognition:

Localrepresentations:N-gramsBag-of-words1-of-NcodingContinuousRepresentations:LatentSemanticAnalysisLatentDirichletAllocationDistributedRepresentations:wordembeddingTomasMikolov,“LearningRepresentationsofTextusingNeuralNetworks”,NIPsDeeplearningWorkshop2023RANLP2023,Hissar,BulgariaWordEmbedding6thSeptember202357Skip-gramArchCBOWThehiddenlayervectoristheword-embeddingvectorforw(t)WordEmbeddingforSentimentDetection

IthasbeenwidelyacceptedasstandardfeaturesforNLPapplicationsincludingsentimentanalysissince2023[Mikolov2023]Thewordvectorspaceimplicitlyencodesmanylinguisticregularitiesamongwords:semanticandsyntacticExample:GooglePre-trainedwordvectorswith1000Billionwords

Doesitencodepolaritysimilarities?great 0.729151bad 0.719005terrific 0.688912decent 0.683735nice 0.683609excellent 0.644293fantastic 0.640778better 0.612073solid 0.580604lousy 0.576420wonderful 0.572612terrible 0.560204Good 0.558616TopRelevantWordsto“good”MostlyYes,butitdoesn’tseparateantonymswellRANLP2023,Hissar,BulgariaLearningSentiment-SpecificWordEmbedding6thSeptember202359Tang,etal,“LearningSentimentSpecificWordEmbeddingforTwitterSentimentClassification”,ACL2023?RANLP2023,Hissar,BulgariaLearningSentiment-SpecificWordEmbedding6thSeptember202360Tang,etal,“LearningSentimentSpecificWordEmbeddingforTwitterSentimentClassification”,ACL2023InSpirit,itissimilartomulti-tasklearning.Itlearnsthesamewayastheregularword-embeddingwithlossfunctionconsideringbothsemanticcontextandsentimentdistancetothetwitteremotionsymbols.10milliontweetsselectedbypositiveandnegativeemoticonsastrainingdataTheTwittersentimentclassificationtrackofSemEval2023LearningSentiment-SpecificWordEmbeddingTang,etal,“LearningSentimentSpecificWordEmbeddingforTwitterSentimentClassification”,ACL2023ParagraphVectorsLeandMikolov,“DistributionalRepresentationsofSentencesandDocuments,ICML2023Paragraphvectorsaredistributionalvectorrepresentationforpiecesoftext,suchassentencesorparagraphsTheparagraphvectorsarealsoaskedtocontributetothepredictiontaskofthenextwordgivenmanycontextssampledfromtheparagraph.EachparagraphcorrespondstoonecolumninDItactsasamemoryrememberingwhatismissingfromthecurrentcontext,aboutthetopicoftheparagraphParagraphVectors–BestResultsonMRDataSetLeandMikolov,“DistributionalRepresentationsofSentencesandDocuments,ICML2023OverviewIntroductionEmotionRecognitioninTextWordEmbeddingforSentimentAnalysisCNNforSentimentClassificationRNN,LSTMforsentimentClassificationPriorKnowledge+CNN/LSTMDatasetCollectionEmotionRecognitioninSpeechEmotionRecognitioninConversationsIndustrialApplicationCNNforSentimentClassification

Ref:YoonKim.ConvolutionalNeuralNetworksforSentenceClassification.EMNLP,2023.CNNforSentimentClassification

Ref:YoonKim.ConvolutionalNeuralNetworksforSentenceClassification.EMNLP,2023.AsimpleCNNwithOneLayerofconvolutionontopofwordvectors.MotivatedbyCNNhasbeensuccessfulonmanyotherNLPtasksInputLayer:Wordvectorsarefrompre-trainedGoogle-Newsword2vectorConvLayer:Windowsize:3words,4words,5words.Eachwith100featuremap.300featuresinthepenultimatelayerPoolingLayer:MaxOvertimePoolingattheOutputlayer:Fullyconnectedsoftmaxlayer,outputdistributionoverlabelsRegularization:Drop-outonthepenultimatelayerwithaconstrainonthel2normsoftheweightvectorsFine-trainembeddingvectorsduringtrainingCommonDatasetsCNNforSentimentClassification-ResultsCNN-rand:RandomlyinitializeallwordembeddingsCNN-static:word2vec,keeptheembeddingsfixedCNN-nonstatic:Fine-tuningembeddingvectorsCNNforSentimentClassification-ResultsWhyitissuccessful?MultiplefiltersandmultiplefeaturemapsEmotionsareexpressedinsegments,insteadofthespanningoverthewholesentenceUsepre-trainedword2vecvectorsasinputfeatures.Embeddingwordvectorsarefurtherimprovedfornon-statictraining.Antonymsarefurtherseparatedaftertraining.ResourcesforThisworkSourceCode:https:///yoonkim/CNN_sentenceImplementationinTensorflow:/dennybritz/cnn-text-classification-tftfExtensiveExperiments:pdfDynamicCNNforSentimentKalchbrenneretal,“AConvolutionalNeuralNetworkforModelingSentences”,ACL2023HyperParametersinExperiments:

K=4m=5,14featuremapsm=7,6featuremapsd=48DynamicCNNforSentimentKalchbrenneretal,“AConvolutionalNeuralNetworkforModelingSentences”,ACL2023OneDimensionConvolution

TwoDimensionConvolution48Dwordvectorsrandomlyinitiated300D

InitiatedwithGoogleword2vectorMorecomplicatedmodelarchitecturewithdynamicpoolingStraightForward

6,4featuremaps100-128featuremapsJohnsonandZhang.,“EffectiveUseofWordOrderforTextCategorizationwithConvolutionalNeuralNetworks”,ACL-2023WhyCNNiseffectiveAsimpleremedyistousewordbi-gramsinadditiontounigramsIthasbeennotedthatlossofwordordercausedbybag-of-wordvectors(bowvectors)isparticularlyproblematiconsentimentclassificationComparingSVMwithTri-gramfeatureswith1,2,3windowfilterCNNTop100FeaturesSVMCNNUni-Grams687Bi-Grams2833Tri-Grams460SVMscan’tfullytakeadvantageofhigh-orderngramsSentimentClassificationConsideringFeaturesbeyondTextwithCNNModelsTangetal.,“LearningSemanticRepresentationsofUsersandProductsforDocumentLevelSentimentClassification“”,ACL-2023OverviewIntroductionEmotionRecognitioninTextWordEmbeddingforSentimentAnalysisCNNforSentimentClassificationRNN,LSTMforsentimentClassificationPriorKnowledge+CNN/LSTMDatasetCollectionEmotionRecognitioninSpeechEmotionRecognitioninConversationsIndustrialApplicationRecursiveNeuralTensorNetworkSocheretal.,“RecursiveDeepModelsforSemanticCompositionalityoveraSentimentTheStanfordSentimentTreebackisacorpuswithfullylabeledparsetreesCreatedtofacilitateanalysisofthecompositionaleffectsofsentimentinlanguage10,662sentencesfrommoviereviews.Parsedbystanfordparser.215,154phrasesarelabeledAmodelcalledRecursiveNeuralTensorNetworkswasproposedRecursiveNeuralTensorNetwork-DistributionofsentimentvaluesforN-gramsSocheretal.,“RecursiveDeepModelsforSemanticCompositionalityoveraSentimentStrongersentimentoftenbuildsupinlongerphrasesandthemajorityoftheshorterphrasesareneutralRecursiveNeuralTensorNetwork(RNTN)Socheretal.,“RecursiveDeepModelsforSemanticCompositionalityoveraSentimentf=tanhVisthetensordirectlyrelateinputvectors,WistheregularRNNweightmatrixWangetal..,“PredictingPolaritiesofTweetsbyComposingWordEmbeddingwithLongShort-TermMemory”,ACL-2023LSTMforSentimentAnalysisLSTMworkstremendouslywellonalargenumberofproblemsSucharchitecturesaremorecapabletolearnacomplexcompositionsuchasnegationofwordvectorsthansimpleRNNs.Input,storedinformation,andoutputarecontrolledbythreegates.Wangetal..,“PredictingPolaritiesofTweetsbyComposingWordEmbeddingwithLongShort-TermMemory”,ACL-2023LSTMforSentimentAnalysisDataset:

theStanfordTwitterSentimentcorpus(STS)LSTM-TLT:Word-embeddingvectorsasinput.TLT:TrainableLook-upTable

Itisobservedthatnegationscanbebettercaptured.Tangetal..,“DocumentModelingwithGatedRecurrentNeuralNetworkforSentimentClassification”,EMNLP-2023GatedRecurrentUnitTangetal..,“DocumentModelingwithGatedRecurrentNeuralNetworkforSentimentClassification”,EMNLP-2023GatedRecurrentNeuralNetworkUseCNN/LSTMtogeneratelsentencerepresentationsfromwordvectors

GateRecurrentNeuralNetwork(GRU)toencodesentencerelationsforsentimentclassificationGRUcanviewedasvariantofLSTM,withoutputgatealwaysonTangetal..,“DocumentModelingwithGatedRecurrentNeuralNetworkforSentimentClassification”,EMNLP-2023GatedRecurrentNeuralNetworkJ.Wangetal.,"DimensionalSentimentAnalysisUsingaRegionalCNN-LSTMModel”,ACL-2023CNN-LSTMJ.Wangetal.,"DimensionalSentimentAnalysisUsingaRegionalCNN-LSTMModel”,ACL-2023CNN-LSTMThedimensionalapproachrepresentsemotionalstatesascontinuousnumericalvaluesinmultipledimensionssuchasthevalence-arousal(VA)space(Russell,1980).Thedimensionofvalencereferstothedegreeofpositiveandnegativesentiment,whereasthedimensionofarousalreferstothedegreeofcalmandexcitementK.STaietal,"ImprovedSemanticRepresentationsFromTree-StructuredLongShort-TermMemoryNetworks”,ACL-2023Tree-LSTMTree-LSTM：

ageneralizationofLSTMstotree-structurednetworktopologies.TreeLSTMsoutperformallexistingsystemsandstrongLSTMbaselinesontwotasks:predictingthesemanticrelatednessoftwosentences(SemEval2023,Task1)andsentimentclassification(StanfordSentimentTreebank).K.STaietal,"ImprovedSemanticRepresentationsFromTree-StructuredLongShort-TermMemoryNetworks”,ACL-2023Tree-LSTMAchievecomparableaccuracyConstituency-TreebasedperformsbetterThewordvectorsareinitializedbyGloveWord2Vectors(Trained/)OverviewIntroductionEmotionRecognitioninTextWordEmbeddingforSentimentAnalysisCNNforSentimentClassificationRNN,LSTMforsentimentClassificationPriorKnowledge+CNN/LSTMDatasetCollectionEmotionRecognitioninSpeechEmotionRecognitioninConversationsIndustrialApplicationRANLP2023,Hissar,BulgariaPriorKnowledge+DeepNeuralNetworks6thSeptember202390Foreachiteration:Theteachernetworkisobtainedbyprojectingthestudentnetworktoarule-regularizedsubspace(reddashedarrow);Thestudentnetworkisupdatedtobalancebetweenemulatingtheteacher’soutputandpredictingthetruelabels(black/bluesolidarrows).Huetal,”HarnessingDeepNeuralNetworkswithLogicRules”,ACL-2023Thisprocessisagnosticthestudentnetwork,applicabletoanyarchitecture:RNN/DNN/CNNRANLP2023,Hissar,BulgariaPriorKnowledge+DeepNeuralNetworks6thSeptember202391Huetal,”HarnessingDeepNeuralNetworkswithLogicRules”,ACL-2023TheTeacherNetworkiscreatedeachiterationbasedontwocriteria:[1]closeenoughtothestudentnetwork[2]reflectallrulesPriorKnowledge+DeepNeuralNetworksAccuracyonSST2Datasets,-Rule-qistheteachernetworkOnedifficultyfortheplainneuralnetworkistoidentifycontrastivesenseinordertocapturethedominantsentimentprecisely.PriorKnowledgeinExperiment:“AButB”,theoverallsentimentisconsistentwiththesentimentofBOverviewIntroductionEmotionRecognitioninTextWordEmbeddingforSentimentAnalysisCNNforSentimentClassificationRNN,LSTMforsentimentClassificationPriorKnowledge+CNN/LSTMDatasetCollectionEmotionRecognitioninSpeechEmotionRecognitioninConversationsIndustrialApplicationTextCorpusforSentimentAnalysisMR:Moviereviewswithonesentenceperreview.Classificationinvolvesdetectingpositive/negativereviews.

SST:StanfordSentimentTreebank—anextensionofMRbutwithtrain/dev/testsplitsprovidedandfine-grainedlabels(verypositive,positive,neutral,negative,verynegative),re-labeledbySocheretal.(2023)

CR:Customerreviewsofvariousproducts(cameras,MP3setc.).Taskistopredictpositive/negativereviews(HuandLiu,2023).

/~liub/FBS/sentiment-analysis.htmlMPQA:OpinionpolaritydetectionsubtaskoftheMPQAdataset(Wiebeetal.,2023)

YelpDatasetChallengein2023and2023

IMDB:TheratingscaleofIMDBdatasetis1-10

TextCorpusforSentimentAnalysisChineseTextCorpusforSentimentAnalysisNewsandblogpostswithEkmanemotions(Wang,2023)Ren-CECpsblogemotioncorpus(Quan&Ren,2023)Thesentencesareannotatedwitheightemotions:joy,expectation,love,surprise,anxiety,sorrow,anger,andhate.2023ChineseMicroblogSentimentAnalysisEvaluation(CMSAE)DatasetofpostsfromSinaWeiboannotatedwithsevenemotions:anger,disgust,fear,happiness,like,sadnessandsurprise.Thetrainset:4000instances(13252sentences)Thetestset:10000instances(32185sentences)

ChineseValence-ArousalTexts(CVAT)

Liang-ChihYu.2023.BuildingChineseAffectiveResources

inValence-ArousalDimensions.(NAACL/HLT-16).SaifM.Mohammad,“ComputationalAnalysisofAffectandEmotioninLanguage”,EMNLP2023

Manuallycreatedlexicalresources?DictionaryofAffect(Whissell)?AffectiveNormsforEnglishWords(Texts)(Bradley&Lang)?HarvardGeneralInquirercategories(Stoneetc.)/~inquirer/?NRCEmotionLexicon(Mohammad&Turney)/WebPages/lexicons.html?MaxDiffSentimentLexicon(Kiritchenko,Zhu,&Mohammad)/WebPages/lexicons.htmlSaifM.Mohammad,“ComputationalAnalysisofAffectandEmotioninLanguage”,EMNLP2023

Sharedtasksatthesentencelevel?SemEval-2023:AffectiveText?SemEval-2023,2023,2023:SentimentAnalysisinTwitter

?SemEval-2023:SentimentAnalysisofFigurativeLanguageinTwitter

?KaggleCompetition:SentimentAnalysisonMoviereviews

SaifM.Mohammad,“ComputationalAnalysisofAffectandEmotioninLanguage”,EMNLP2023

OtherResources:Affectcorpora?AffectiveTextDataset(Strapparava&Mihalcea)–news;headlines

/~mihalcea/downloads.html#affective?AffectDataset(Alm)–classicliterarytales;sentences

/~coagla/?2023USPresidentialElections–tweets(Mohammadetal.)

/WebDocs/ElectoralTweetsData.zip?EmotionalProsodySpeechandTranscripts–actors/numbers(Libermanetal.)

?HUMAINE–multimodal(Douglas-Cowieetal.)

/download/pilot-db/Other:?EmotionML(Schr?deretal.)?ACII(multipledataformats),Interspeech(spokenlanguage)?IEEETrans.onAffectiveComp.SaifM.Mohammad,“ComputationalAnalysisofAffectandEmotioninLanguage”,EMNLP2023

OverviewIntroductionEmotionRecognitioninTextEmotionRecognitioninSpeechThecommonframeworkDNNforspeechemotionrecognitionRNNforspeechemotionrecognitionCNNforspeechemotionrecognitionDatacollectionforspeechemotionrecognitionEmotionRecognitioninConversationsIndustrialApplicationTheCommonFrameworkStep1:SegmentLevelStep2:Utterance

LevelClassifierCNNDNN/LSTMRNN/ELMThecommonfeaturesFramefeatureset:Framelength:25ms,with10msslidingSegmentlength:265ms,enoughtoexpressemotionINTERSPEECH2023EmotionChallengeFeatureset:12MFCC;F0,root-mean-squaresignalframeenergy;zero-crossingrateoftimesignalandthevoicingprobabilitycomputedfromtheACF[?].1storderderivatives;acousticfeatures:Segmentlength:250ms;stackframefeatures

Classifier,DistributionofemotionstatesOverviewIntroductionEmotionRecognitioninTextEmotionRecognitioninSpeechThecommonframeworkDNNforspeechemotionrecognitionRNNforspeechemotionrecognitionCNNforspeechemotionrecognitionDatacollectionforspeechemotionrecognitionEmotionRecognitioninConversationsIndustrialApplicationDBN+iVectorRuiXiaandYangliu,"DBN-ivectorFrameworkforAcousticEmotionRecognition”,

Interspeech2023DNN+ELMFrame-levelfeatures:30commonacousticfeaturesSegment-levelfeatures:Stacksoflowlevelframe-basedfeatures.DNN

asaclassifiertoseparatepositiveandnegativeUtterance-levelfeatures:statisticsofthesegment-levelprobabilities,maximal,minimalandmeanofsegment-levelprobabilityofthekthemotionovertheutterance,thepercentageofsegmentswhichhavehighprobabilityofemotionkK.Han,DYuandI.Tashev,“SpeechEmotionRecognitionUsingDeepNeuralNetworkandExtremeLearningMachine

“

，Interspeech2023DNN+ELMFrame–Level:InputLayer:750Units(25frames,30LLDfeaturesperframe)HiddenLayers:3layers;256Reluneuronsperlayer;OutputLayer:5emotions(excitement,frustration,happiness,neutralandsurprise）Training:Mini-batchgradientdescendmethod,cross-entropyastheobjectivefunctionK.Han,DYuandI.Tashev,“SpeechEmotionRecognitionUsingDeepNeuralNetworkandExtremeLearningMachine

“

，Interspeech2023DNN+ELMUtteranceLevel:ExtremelearningmachineInputLayer:4statisticsx5emotionsHiddenLayers:1layer;120OutputLayer:5emotions(excitement,frustration,happiness,neutralandsurprise）Training:SuperFastK.Han,DYuandI.Tashev,“SpeechEmotionRecognitionUsingDeepNeuralNetworkandExtremeLearningMachine

“

，Interspeech2023DNN+ELMK.Han,DYuandI.Tashev,“SpeechEmotionRecognitionUsingDeepNeuralNetworkandExtremeLearningMachine

“

，Interspeech2023EmotionalDyadicMotionCapture(IEMOCAP)database[referredincomments]toevaluateourapproach.Thedatabasecontainsaudiovisualdatafrom1