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.- Anthology ID:
- 2023.arabicnlp-1.54
- 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:
- SIGARAB
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 549–557
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2023.arabicnlp-1.54/
- DOI:
- 10.18653/v1/2023.arabicnlp-1.54
- Cite (ACL):
- Fatima Zahra Qachfar and Rakesh Verma. 2023. ReDASPersuasion at ArAIEval Shared Task: Multilingual and Monolingual Models For Arabic Persuasion Detection. In Proceedings of ArabicNLP 2023, pages 549–557, Singapore (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- ReDASPersuasion at ArAIEval Shared Task: Multilingual and Monolingual Models For Arabic Persuasion Detection (Qachfar & Verma, ArabicNLP 2023)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2023.arabicnlp-1.54.pdf