Pythoneers at WANLP 2022 Shared Task: Monolingual AraBERT for Arabic Propaganda Detection and Span Extraction

Joseph Attieh, Fadi Hassan


Abstract
In this paper, we present two deep learning approaches that are based on AraBERT, submitted to the Propaganda Detection shared task of the Seventh Workshop for Arabic Natural Language Processing (WANLP 2022). Propaganda detection consists of two main sub-tasks, mainly propaganda identification and span extraction. We present one system per sub-task. The first system is a Multi-Task Learning model that consists of a shared AraBERT encoder with task-specific binary classification layers. This model is trained to jointly learn one binary classification task per propaganda method. The second system is an AraBERT model with a Conditional Random Field (CRF) layer. We achieved rank 3 on the first sub-task and rank 1 on the second sub-task.
Anthology ID:
2022.wanlp-1.64
Volume:
Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Editors:
Houda Bouamor, Hend Al-Khalifa, Kareem Darwish, Owen Rambow, Fethi Bougares, Ahmed Abdelali, Nadi Tomeh, Salam Khalifa, Wajdi Zaghouani
Venue:
WANLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
534–540
Language:
URL:
https://aclanthology.org/2022.wanlp-1.64
DOI:
10.18653/v1/2022.wanlp-1.64
Bibkey:
Cite (ACL):
Joseph Attieh and Fadi Hassan. 2022. Pythoneers at WANLP 2022 Shared Task: Monolingual AraBERT for Arabic Propaganda Detection and Span Extraction. In Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP), pages 534–540, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
Pythoneers at WANLP 2022 Shared Task: Monolingual AraBERT for Arabic Propaganda Detection and Span Extraction (Attieh & Hassan, WANLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.wanlp-1.64.pdf