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1、Cross-TaskCrowdsourcingKaixiangMo1,ErhengZhong1andQiangYang1,21:HongKongUniversityofScienceandTechnology,HongKong;2:HuaweiNoah’sArkLab,ScienceandTechnologyPark,Shatin,HongKong{kxmo,ezhong,qyang}@cse.ust.hkABSTRACTKeywordsCrowdsourcingisaneffectivemethodforcollectinglabeledCrowdsourcing,
2、TransferLearningdataforvariousdataminingtasks.Itiscriticaltoensuretheveracityoftheproduceddatabecauseresponsescollected1.INTRODUCTIONfromdifferentusersmaybenoisyandunreliable.PreviousworkssolvethisveracityproblembyestimatingboththeProducinglarge-scaletraining,validationandtestsetsisuser
3、abilityandquestiondifficultybasedontheknowledgevitalformanymachinelearninganddataminingapplica-ineachtaskindividually.Inthiscase,eachsingletaskneedstions.Mostoftenthistaskhastobecarriedout“byhand”largeamountsofdatatoprovideaccurateestimations.How-andthusitisdelicate,expensive,andtedious.
4、Crowd-1ever,inpractice,budgetsprovidedbycustomersforagivensourcingsystemssuchasAmazonMechanicalTurk,re-23targettaskmaybelimited,andhenceeachquestioncanbeCAPTCHAandtheESPgamehavemadeiteasytodis-presentedtoonlyafewuserswhereeachusercananswertributesimplelabelingtaskstohundredsofusers(ref
5、erredonlyafewquestions.ThisdatasparsityproblemcancausetoasworkerinMechanicalTurk).Crowdsourcingiswidelypreviousapproachestoperformpoorlyduetotheoverfit-usedintaskssuchassentimentclassification[12],objecttingproblemonraredataandeventuallydamagethedatarecognition[7],ranking[13]andclusterin
6、g[6],etc.veracity.Fortunately,inreal-worldapplications,userscanAtypicalcrowdsourcingpipelinecanbedividedintothreeanswerquestionsfrommultiplehistoricaltasks.Forexam-mainsteps:1:Taskdesign,2:Datadistributionand3:An-ple,onecanannotateimagesaswellaslabelthesentimentsweraggregation.Intheans
7、weraggregationstep,oneneedsofagiventitle.Inthispaper,weemploytransferlearning,toaggregatethesenoisyandunreliableresponsesintoatruewhichborrowsknowledgefromauxiliaryhistoricaltaskstoanswer.Answeraggregationismostimportantstepbecauseimprovethedataveracityinagiventargettask.Themoti-itis
