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基于协同学习的跨语言隐式篇章关系识别（英文）.docx

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基于协同学习的跨语言隐式篇章关系识别（英文） Title:Cross-LanguageImplicitDiscourseRelationshipRecognitionBasedonCollaborativeLearning Abstract: Recognizingimplicitdiscourserelationshipsisvitalforvariousnaturallanguageprocessingtaskssuchassentimentanalysis,informationretrieval,andquestionanswering.However,solvingthistaskinacross-languagecontextposessignificantchallengesduetolanguagedifferencesandlimitedlabeleddataforcertainlanguagepairs.Inthispaper,weproposeanovelapproachforcross-languageimplicitdiscourserelationshiprecognitionbasedoncollaborativelearning.Ourmethodleveragestheknowledgeoflabeleddatafromresource-richlanguagestoimprovetheperformanceinresource-scarcelanguages.Experimentalresultsonamultilingualdatasetdemonstratetheeffectivenessofourapproachincapturingimplicitdiscourserelationshipsacrossdifferentlanguages. 1.Introduction Implicitdiscourserelationshiprecognitionaimstoidentifytheconnectionsbetweendifferentsegmentsoftextwithoutthepresenceofexplicitmarkersorindicators.Thistaskplaysacrucialroleinunderstandingtheflowandcoherenceofadocument,enablingvariousdownstreamapplications.However,performingthisrecognitionacrossdifferentlanguagespresentsseveralchallenges,includinglanguagedifferences,scarcityoflabeleddata,andculturaldivergences.Inthispaper,weproposeacollaborativelearningapproachtoaddressthesechallengesandimprovecross-languageimplicitdiscourserelationshiprecognition. 2.RelatedWork Priorresearchonimplicitdiscourserelationshiprecognitionhasmainlyfocusedonmonolingualscenarios,neglectingtheintricaciesofcross-languagesettings.Approachessuchassupervisedlearning,unsupervisedlearning,andneuralnetworkmodelshavebeenexploredintheliterature.Moreover,thereexiststudiesoncross-lingualtransferlearninganddomainadaptation.However,fewworkshavecombinedtheseapproachestotackletheproblemofcross-languagerecognitionofimplicitdiscourserelationshipseffectively. 3.Methodology Ourproposedcollaborativelearningapproachconsistsoftwokeysteps:(1)leveraginglabeleddatafromresource-richlanguages,and(2)transferringthel