@inproceedings{qachfar-verma-2023-redaspersuasion-araieval,
title = "{R}e{DASP}ersuasion at {A}r{AIE}val Shared Task: Multilingual and Monolingual Models For {A}rabic Persuasion Detection",
author = "Qachfar, Fatima Zahra and
Verma, Rakesh",
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-1.54",
doi = "10.18653/v1/2023.arabicnlp-1.54",
pages = "549--557",
abstract = "To enhance persuasion detection, we investigate the use of multilingual systems on Arabic data by conducting a total of 22 experiments using baselines, multilingual, and monolingual language transformers. Our aim is to provide a comprehensive evaluation of the various systems employed throughout this task, with the ultimate goal of comparing their performance and identifying the most effective approach. Our empirical analysis shows that *ReDASPersuasion* system performs best when combined with multilingual {``}XLM-RoBERTa{''} and monolingual pre-trained transformers on Arabic dialects like {``}CAMeLBERT-DA SA{''} depending on the NLP classification task.",
}
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<abstract>To enhance persuasion detection, we investigate the use of multilingual systems on Arabic data by conducting a total of 22 experiments using baselines, multilingual, and monolingual language transformers. Our aim is to provide a comprehensive evaluation of the various systems employed throughout this task, with the ultimate goal of comparing their performance and identifying the most effective approach. Our empirical analysis shows that *ReDASPersuasion* system performs best when combined with multilingual “XLM-RoBERTa” and monolingual pre-trained transformers on Arabic dialects like “CAMeLBERT-DA SA” depending on the NLP classification task.</abstract>
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%0 Conference Proceedings
%T ReDASPersuasion at ArAIEval Shared Task: Multilingual and Monolingual Models For Arabic Persuasion Detection
%A Qachfar, Fatima Zahra
%A Verma, Rakesh
%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 qachfar-verma-2023-redaspersuasion-araieval
%X To enhance persuasion detection, we investigate the use of multilingual systems on Arabic data by conducting a total of 22 experiments using baselines, multilingual, and monolingual language transformers. Our aim is to provide a comprehensive evaluation of the various systems employed throughout this task, with the ultimate goal of comparing their performance and identifying the most effective approach. Our empirical analysis shows that *ReDASPersuasion* system performs best when combined with multilingual “XLM-RoBERTa” and monolingual pre-trained transformers on Arabic dialects like “CAMeLBERT-DA SA” depending on the NLP classification task.
%R 10.18653/v1/2023.arabicnlp-1.54
%U https://aclanthology.org/2023.arabicnlp-1.54
%U https://doi.org/10.18653/v1/2023.arabicnlp-1.54
%P 549-557
Markdown (Informal)
[ReDASPersuasion at ArAIEval Shared Task: Multilingual and Monolingual Models For Arabic Persuasion Detection](https://aclanthology.org/2023.arabicnlp-1.54) (Qachfar & Verma, ArabicNLP-WS 2023)
ACL