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基于改进AttentionMask编解码器CPI的研究
Abstract
AttentionMaskEncoder-DecoderConvolutionalNeuralNetwork(CPI)isanaturallanguageprocessingmodelthatusesself-attentionmechanismandconvolutionalneuralnetworktoextractsequentialfeaturesandgenerateaccuratepredictions.Inthispaper,weproposeanimprovedversionoftheCPImodelbyincorporatingamorepowerfulattentionmechanismcalledMulti-HeadAttention,whichallowsthemodeltofocusonmultipleaspectsoftheinputdatasimultaneously.Wealsointroducearesidualconnectionbetweentheencoderanddecoderlayerstoenhancetheflowofinformationandpreventthevanishinggradientproblem.ExperimentalresultsonthebenchmarkdatasetshowthattheproposedCPImodelachievesbetterperformancethantheoriginalmodelandstate-of-the-artmodelsintermsofaccuracyandefficiency.
Introduction
Naturallanguageprocessing(NLP)hasemergedasacrucialtaskwiththeadventofdeeplearningmethodsthatcanlearnhierarchicalrepresentationsoftextdata.Translation,summarization,andimagecaptioningaresomeofthepopularNLPtasksthathavebeenaddressedusingdeeplearningmodels.Encoder-DecodermodelshavebeenwidelyusedintheNLPdomaintolearntherepresentationoftheinputsequenceandgeneratethecorrespondingoutputsequence.
TheCPImodelintroducedbySuetal.[1]isonesuchEncoder-Decodermodelthathasbeenusedtogenerateaccuratepredictionsforsequence-to-sequencetaskssuchasmachinetranslation.Thismodelusesacombinationofconvolutionalneuralnetwork(CNN)andself-attentionmechanismtoextractsequentialfeaturesfromtheinputsequenceandgeneratetheoutputsequence.Theself-attentionmechanismenablesthemodeltofocusontherelevantpartsoftheinputsequencewhilegeneratingtheoutputsequence.
AlthoughtheoriginalCPImodelachievesgoodperformanceonvariousNLPtasks,itsuffersfromsomelimitations.Firstly,themodelonlyusessingle-headattention,whichlimitsthecapacityofthemodeltocapturecomplexrelationshipswithintheinputsequence.Secondly,themodelsuffersfromtheproblemofvanishinggradients,whichhinderstheflowofinformationacrosstheencoderanddecoderlayers.
Inthispaper,weproposeanimprovedversionoftheCPImodelbyincorpora