Abstract
This paper presents SI2M & AIOX Labs work among the propaganda detection in Arabic text shared task. The objective of this challenge is to identify the propaganda techniques used in specific propaganda fragments. We use a combination of data augmentation, Name Entity Recognition, rule-based repetition detection, and ARBERT prediction to develop our system. The model we provide scored 0.585 micro F1-Score and ranked 6th out of 12 teams.- Anthology ID:
- 2022.wanlp-1.58
- 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:
- 506–510
- Language:
- URL:
- https://aclanthology.org/2022.wanlp-1.58
- DOI:
- 10.18653/v1/2022.wanlp-1.58
- Cite (ACL):
- Kamel Gaanoun and Imade Benelallam. 2022. SI2M & AIOX Labs at WANLP 2022 Shared Task: Propaganda Detection in Arabic, A Data Augmentation and Name Entity Recognition Approach. In Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP), pages 506–510, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- SI2M & AIOX Labs at WANLP 2022 Shared Task: Propaganda Detection in Arabic, A Data Augmentation and Name Entity Recognition Approach (Gaanoun & Benelallam, WANLP 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.wanlp-1.58.pdf