@inproceedings{deka-revi-2023-pd,
title = "{PD}-{AR} at {A}r{AIE}val Shared Task: A {BERT}-Centric Approach to Tackle {A}rabic Disinformation",
author = "Deka, Pritam and
Revi, Ashwathy",
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-1.57",
doi = "10.18653/v1/2023.arabicnlp-1.57",
pages = "570--575",
abstract = "This work explores Arabic disinformation identification, a crucial task in natural language processing, using a state-of-the-art NLP model. We highlight the performance of our system model against baseline models, including multilingual and Arabic-specific ones, and showcase the effectiveness of domain-specific pre-trained models. This work advocates for the adoption of tailored pre-trained models in NLP, emphasizing their significance in understanding diverse languages. By merging advanced NLP techniques with domain-specific pre-training, it advances Arabic disinformation identification.",
}
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<abstract>This work explores Arabic disinformation identification, a crucial task in natural language processing, using a state-of-the-art NLP model. We highlight the performance of our system model against baseline models, including multilingual and Arabic-specific ones, and showcase the effectiveness of domain-specific pre-trained models. This work advocates for the adoption of tailored pre-trained models in NLP, emphasizing their significance in understanding diverse languages. By merging advanced NLP techniques with domain-specific pre-training, it advances Arabic disinformation identification.</abstract>
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%0 Conference Proceedings
%T PD-AR at ArAIEval Shared Task: A BERT-Centric Approach to Tackle Arabic Disinformation
%A Deka, Pritam
%A Revi, Ashwathy
%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 deka-revi-2023-pd
%X This work explores Arabic disinformation identification, a crucial task in natural language processing, using a state-of-the-art NLP model. We highlight the performance of our system model against baseline models, including multilingual and Arabic-specific ones, and showcase the effectiveness of domain-specific pre-trained models. This work advocates for the adoption of tailored pre-trained models in NLP, emphasizing their significance in understanding diverse languages. By merging advanced NLP techniques with domain-specific pre-training, it advances Arabic disinformation identification.
%R 10.18653/v1/2023.arabicnlp-1.57
%U https://aclanthology.org/2023.arabicnlp-1.57
%U https://doi.org/10.18653/v1/2023.arabicnlp-1.57
%P 570-575
Markdown (Informal)
[PD-AR at ArAIEval Shared Task: A BERT-Centric Approach to Tackle Arabic Disinformation](https://aclanthology.org/2023.arabicnlp-1.57) (Deka & Revi, ArabicNLP-WS 2023)
ACL