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.- Anthology ID:
- 2023.arabicnlp-1.57
- Volume:
- Proceedings of ArabicNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore (Hybrid)
- Editors:
- Hassan Sawaf, Samhaa El-Beltagy, Wajdi Zaghouani, Walid Magdy, Ahmed Abdelali, Nadi Tomeh, Ibrahim Abu Farha, Nizar Habash, Salam Khalifa, Amr Keleg, Hatem Haddad, Imed Zitouni, Khalil Mrini, Rawan Almatham
- Venues:
- ArabicNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 570–575
- Language:
- URL:
- https://aclanthology.org/2023.arabicnlp-1.57
- DOI:
- 10.18653/v1/2023.arabicnlp-1.57
- Cite (ACL):
- Pritam Deka and Ashwathy Revi. 2023. PD-AR at ArAIEval Shared Task: A BERT-Centric Approach to Tackle Arabic Disinformation. In Proceedings of ArabicNLP 2023, pages 570–575, Singapore (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- PD-AR at ArAIEval Shared Task: A BERT-Centric Approach to Tackle Arabic Disinformation (Deka & Revi, ArabicNLP-WS 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.arabicnlp-1.57.pdf