Danish Asylum Adjudication using Deep Neural Networks and Natural Language Processing

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Standard

Danish Asylum Adjudication using Deep Neural Networks and Natural Language Processing. / Muddamsetty, Satya Mahesh; Jahromi, Mohammad Naser Sabet; Moeslund, Thomas B.; Gammeltoft-Hansen, Thomas.

Danish Asylum Adjudication using Deep Neural Networks and Natural Language Processing. Springer, 2023. s. 1-13.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Harvard

Muddamsetty, SM, Jahromi, MNS, Moeslund, TB & Gammeltoft-Hansen, T 2023, Danish Asylum Adjudication using Deep Neural Networks and Natural Language Processing. i Danish Asylum Adjudication using Deep Neural Networks and Natural Language Processing. Springer, s. 1-13. <https://vbn.aau.dk/en/publications/danish-asylum-adjudication-using-deep-neural-networks-and-natural>

APA

Muddamsetty, S. M., Jahromi, M. N. S., Moeslund, T. B., & Gammeltoft-Hansen, T. (Accepteret/In press). Danish Asylum Adjudication using Deep Neural Networks and Natural Language Processing. I Danish Asylum Adjudication using Deep Neural Networks and Natural Language Processing (s. 1-13). Springer. https://vbn.aau.dk/en/publications/danish-asylum-adjudication-using-deep-neural-networks-and-natural

Vancouver

Muddamsetty SM, Jahromi MNS, Moeslund TB, Gammeltoft-Hansen T. Danish Asylum Adjudication using Deep Neural Networks and Natural Language Processing. I Danish Asylum Adjudication using Deep Neural Networks and Natural Language Processing. Springer. 2023. s. 1-13

Author

Muddamsetty, Satya Mahesh ; Jahromi, Mohammad Naser Sabet ; Moeslund, Thomas B. ; Gammeltoft-Hansen, Thomas. / Danish Asylum Adjudication using Deep Neural Networks and Natural Language Processing. Danish Asylum Adjudication using Deep Neural Networks and Natural Language Processing. Springer, 2023. s. 1-13

Bibtex

@inproceedings{a17ee606d37645509302dff88eb31e3f,

title = "Danish Asylum Adjudication using Deep Neural Networks and Natural Language Processing",

abstract = "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.",

author = "Muddamsetty, {Satya Mahesh} and Jahromi, {Mohammad Naser Sabet} and Moeslund, {Thomas B.} and Thomas Gammeltoft-Hansen",

year = "2023",

language = "English",

pages = "1--13",

booktitle = "Danish Asylum Adjudication using Deep Neural Networks and Natural Language Processing",

publisher = "Springer",

address = "Switzerland",

}

RIS

TY - GEN

T1 - Danish Asylum Adjudication using Deep Neural Networks and Natural Language Processing

AU - Muddamsetty, Satya Mahesh

AU - Jahromi, Mohammad Naser Sabet

AU - Moeslund, Thomas B.

AU - Gammeltoft-Hansen, Thomas

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

M3 - Article in proceedings

SP - 1

EP - 13

BT - Danish Asylum Adjudication using Deep Neural Networks and Natural Language Processing

PB - Springer

ER -

ID: 377826428