@inproceedings{abdel-salam-2023-rematchka,
title = "rematchka at {A}r{AIE}val Shared Task: Prefix-Tuning {\&} Prompt-tuning for Improved Detection of Propaganda and Disinformation in {A}rabic Social Media Content",
author = "Abdel-Salam, Reem",
editor = "Sawaf, Hassan and
El-Beltagy, Samhaa and
Zaghouani, Wajdi and
Magdy, Walid and
Abdelali, Ahmed and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Habash, Nizar and
Khalifa, Salam and
Keleg, Amr and
Haddad, Hatem and
Zitouni, Imed and
Mrini, Khalil and
Almatham, Rawan",
booktitle = "Proceedings of ArabicNLP 2023",
month = dec,
year = "2023",
address = "Singapore (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.arabicnlp-1.52",
doi = "10.18653/v1/2023.arabicnlp-1.52",
pages = "536--542",
abstract = "The rise of propaganda and disinformation in the digital age has necessitated the development of effective detection methods to combat the spread of deceptive information. In this paper we present our approach proposed for ArAIEval shared task : propaganda and disinformation detection in Arabic text. Our system utilised different pre-trained BERT based models, that makes use of prompt-learning based on knowledgeable expansion and prefix-tuning. The proposed approach secured third place in subtask-1A with 0.7555 F1-micro score, second place in subtask-1B with 0.5658 F1-micro score. However, for subtask-2A {\&} 2B, the proposed system achieved fourth place with an F1-micro score of 0.9040, 0.8219 respectively. Our findings suggest that prompt-tuning-based {\&} prefix-tuning based models performed better than conventional fine-tuning. Furthermore, using loss aware class imbalance, improved performance.",
}
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<abstract>The rise of propaganda and disinformation in the digital age has necessitated the development of effective detection methods to combat the spread of deceptive information. In this paper we present our approach proposed for ArAIEval shared task : propaganda and disinformation detection in Arabic text. Our system utilised different pre-trained BERT based models, that makes use of prompt-learning based on knowledgeable expansion and prefix-tuning. The proposed approach secured third place in subtask-1A with 0.7555 F1-micro score, second place in subtask-1B with 0.5658 F1-micro score. However, for subtask-2A & 2B, the proposed system achieved fourth place with an F1-micro score of 0.9040, 0.8219 respectively. Our findings suggest that prompt-tuning-based & prefix-tuning based models performed better than conventional fine-tuning. Furthermore, using loss aware class imbalance, improved performance.</abstract>
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%0 Conference Proceedings
%T rematchka at ArAIEval Shared Task: Prefix-Tuning & Prompt-tuning for Improved Detection of Propaganda and Disinformation in Arabic Social Media Content
%A Abdel-Salam, Reem
%Y Sawaf, Hassan
%Y El-Beltagy, Samhaa
%Y Zaghouani, Wajdi
%Y Magdy, Walid
%Y Abdelali, Ahmed
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Habash, Nizar
%Y Khalifa, Salam
%Y Keleg, Amr
%Y Haddad, Hatem
%Y Zitouni, Imed
%Y Mrini, Khalil
%Y Almatham, Rawan
%S Proceedings of ArabicNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore (Hybrid)
%F abdel-salam-2023-rematchka
%X The rise of propaganda and disinformation in the digital age has necessitated the development of effective detection methods to combat the spread of deceptive information. In this paper we present our approach proposed for ArAIEval shared task : propaganda and disinformation detection in Arabic text. Our system utilised different pre-trained BERT based models, that makes use of prompt-learning based on knowledgeable expansion and prefix-tuning. The proposed approach secured third place in subtask-1A with 0.7555 F1-micro score, second place in subtask-1B with 0.5658 F1-micro score. However, for subtask-2A & 2B, the proposed system achieved fourth place with an F1-micro score of 0.9040, 0.8219 respectively. Our findings suggest that prompt-tuning-based & prefix-tuning based models performed better than conventional fine-tuning. Furthermore, using loss aware class imbalance, improved performance.
%R 10.18653/v1/2023.arabicnlp-1.52
%U https://aclanthology.org/2023.arabicnlp-1.52
%U https://doi.org/10.18653/v1/2023.arabicnlp-1.52
%P 536-542
Markdown (Informal)
[rematchka at ArAIEval Shared Task: Prefix-Tuning & Prompt-tuning for Improved Detection of Propaganda and Disinformation in Arabic Social Media Content](https://aclanthology.org/2023.arabicnlp-1.52) (Abdel-Salam, ArabicNLP-WS 2023)
ACL