@inproceedings{fouad-weeds-2024-sussexai,
title = "{S}ussex{AI} at {A}r{AIE}val Shared Task: Mitigating Class Imbalance in {A}rabic Propaganda Detection",
author = "Fouad, Mary and
Weeds, Julie",
editor = "Habash, Nizar and
Bouamor, Houda and
Eskander, Ramy and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Abdelali, Ahmed and
Touileb, Samia and
Hamed, Injy and
Onaizan, Yaser and
Alhafni, Bashar and
Antoun, Wissam and
Khalifa, Salam and
Haddad, Hatem and
Zitouni, Imed and
AlKhamissi, Badr and
Almatham, Rawan and
Mrini, Khalil",
booktitle = "Proceedings of The Second Arabic Natural Language Processing Conference",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.arabicnlp-1.55",
pages = "524--529",
abstract = "In this paper, we are exploring mitigating class imbalancein Arabic propaganda detection. Given amultigenre text which could be a news paragraphor a tweet, the objective is to identify the propagandatechnique employed in the text along withthe exact span(s) where each technique occurs. Weapproach this task as a sequence tagging task. Weutilise AraBERT for sequence classification andimplement data augmentation and random truncationmethods to mitigate the class imbalance withinthe dataset. We demonstrate the importance ofconsidering macro-F1 as well as micro-F1 whenevaluating classifier performance in this scenario.",
}
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<abstract>In this paper, we are exploring mitigating class imbalancein Arabic propaganda detection. Given amultigenre text which could be a news paragraphor a tweet, the objective is to identify the propagandatechnique employed in the text along withthe exact span(s) where each technique occurs. Weapproach this task as a sequence tagging task. Weutilise AraBERT for sequence classification andimplement data augmentation and random truncationmethods to mitigate the class imbalance withinthe dataset. We demonstrate the importance ofconsidering macro-F1 as well as micro-F1 whenevaluating classifier performance in this scenario.</abstract>
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%0 Conference Proceedings
%T SussexAI at ArAIEval Shared Task: Mitigating Class Imbalance in Arabic Propaganda Detection
%A Fouad, Mary
%A Weeds, Julie
%Y Habash, Nizar
%Y Bouamor, Houda
%Y Eskander, Ramy
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Abdelali, Ahmed
%Y Touileb, Samia
%Y Hamed, Injy
%Y Onaizan, Yaser
%Y Alhafni, Bashar
%Y Antoun, Wissam
%Y Khalifa, Salam
%Y Haddad, Hatem
%Y Zitouni, Imed
%Y AlKhamissi, Badr
%Y Almatham, Rawan
%Y Mrini, Khalil
%S Proceedings of The Second Arabic Natural Language Processing Conference
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F fouad-weeds-2024-sussexai
%X In this paper, we are exploring mitigating class imbalancein Arabic propaganda detection. Given amultigenre text which could be a news paragraphor a tweet, the objective is to identify the propagandatechnique employed in the text along withthe exact span(s) where each technique occurs. Weapproach this task as a sequence tagging task. Weutilise AraBERT for sequence classification andimplement data augmentation and random truncationmethods to mitigate the class imbalance withinthe dataset. We demonstrate the importance ofconsidering macro-F1 as well as micro-F1 whenevaluating classifier performance in this scenario.
%U https://aclanthology.org/2024.arabicnlp-1.55
%P 524-529
Markdown (Informal)
[SussexAI at ArAIEval Shared Task: Mitigating Class Imbalance in Arabic Propaganda Detection](https://aclanthology.org/2024.arabicnlp-1.55) (Fouad & Weeds, ArabicNLP-WS 2024)
ACL