TheDanishasylumadjudicationprocedure isatwo-tiered system,withtheImmigrationServicemakinginitialdeterminationsand theDanishRefugeeAppealsBoard(RAB)automaticallyappealingcases thatarerejected.Thisstudyaimstoemployadeepneuralnetwork(DNN)basedNaturalLanguageProcessing(NLP)pipeline topredictasylum decision-makingoutcomesusingadataset of over 15,515DanishasylumdecisionsprovidedbytheDanishRefugeeAppealsBoard(RAB) betweenJanuary1995andJanuary2021.Thisresearchseekstoimprove theperformanceandeffectivenessofdecision-makinginasylumcasesby addressingkeychallenges,suchasmodelingtheasylumdecision-making problemusingNLP-basedDNNsanddealingwithclassimbalanceissues. OurpreliminaryresultsindicatethatDNN-basedNLPpredictivemodels arecapableof learningmeaningful representationsofasylumcaseswith highprecisionandrecall,particularlywhenclassweightsareconsidered thanthebaselineDNNmodel.