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基于深度学习的问答研究的开题报告.docx

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基于深度学习的问答研究的开题报告 Abstract Withthedevelopmentofdeeplearningtechnology,questionanswering(QA)systemsbasedondeeplearninghaveachievedsignificantprogressinrecentyears.Inthisproposal,weintroducethecurrentresearchstatusandthechallengesofQAsystemsbasedondeeplearning,andproposearesearchplantoaddressthechallenges. Introduction Questionansweringsystemisoneofthemostimportantapplicationsofnaturallanguageprocessing(NLP).ThegoalofQAsystemsistoautomaticallyansweruser'squestionsbyunderstandingthenaturallanguage.Withthedevelopmentofdeeplearningtechnology,QAsystemsbasedondeeplearninghaveachievedsignificantprogressinrecentyears.Inthisproposal,weintroducethecurrentresearchstatusandthechallengesofQAsystemsbasedondeeplearning,andproposearesearchplantoaddressthechallenges. Currentresearchstatus Currently,mostQAsystemsbasedondeeplearningadopttheend-to-endapproach,whichconsistsoftwomainmodules:thereadingcomprehensionmoduleandtheanswergenerationmodule.Inthereadingcomprehensionmodule,thesystemaimstofindtherelevantpassageordocumentthatcontainstheanswertotheuser'squestion.Thismoduleisusuallyimplementedusingneuralnetworkmodelssuchasrecurrentneuralnetworks(RNNs),convolutionalneuralnetworks(CNNs),andattentionmechanisms.Intheanswergenerationmodule,giventherelevantpassage,thesystemaimstogeneratethecorrectanswertotheuser'squestion.ThismoduleisalsoimplementedusingneuralnetworkmodelssuchasRNNsandtransformer-basedmodels. Challenges Despitetheprogressmadeinrecentyears,therearestillseveralchallengesthatneedtobeaddressedinQAsystemsbasedondeeplearning.Themainchallengesareasfollows: 1.Limitedtrainingdata:MostQAdatasetsaresmallinsize,whichmakesitdifficulttotraindeeplearningmodelsthatrequirelargeamountsofdata.Thislimitstheeffectivenessofdeeplearningmodelsinquestionanswering. 2.Domain-specificknowledge:Manyquestionsrequiredomain-specificknowledge,whichisnotpresentingenerallanguagemodels.ThismakesitdifficultforQAsystemstoanswersuchquestions. 3.Incorporatingexternalknowledge:Sometimes,QAsystemsneedtoincorporateexte