CNLP-NITS-PP at WANLP 2022 Shared Task: Propaganda Detection in Arabic using Data Augmentation and AraBERT Pre-trained Model
Sahinur Rahman Laskar, Rahul Singh, Abdullah Faiz Ur Rahman Khilji, Riyanka Manna, Partha Pakray, Sivaji Bandyopadhyay
Abstract
In today’s time, online users are regularly exposed to media posts that are propagandistic. Several strategies have been developed to promote safer media consumption in Arabic to combat this. However, there is a limited available multilabel annotated social media dataset. In this work, we have used a pre-trained AraBERT twitter-base model on an expanded train data via data augmentation. Our team CNLP-NITS-PP, has achieved the third rank in subtask 1 at WANLP-2022, for propaganda detection in Arabic (shared task) in terms of micro-F1 score of 0.602.- Anthology ID:
- 2022.wanlp-1.65
- 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:
- 541–544
- Language:
- URL:
- https://aclanthology.org/2022.wanlp-1.65
- DOI:
- 10.18653/v1/2022.wanlp-1.65
- Cite (ACL):
- Sahinur Rahman Laskar, Rahul Singh, Abdullah Faiz Ur Rahman Khilji, Riyanka Manna, Partha Pakray, and Sivaji Bandyopadhyay. 2022. CNLP-NITS-PP at WANLP 2022 Shared Task: Propaganda Detection in Arabic using Data Augmentation and AraBERT Pre-trained Model. In Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP), pages 541–544, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- CNLP-NITS-PP at WANLP 2022 Shared Task: Propaganda Detection in Arabic using Data Augmentation and AraBERT Pre-trained Model (Laskar et al., WANLP 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.wanlp-1.65.pdf