Abstract
This paper presents the ArabicProcessors team’s deep learning system designed for the NADI 2020 Subtask 1 (country-level dialect identification) and Subtask 2 (province-level dialect identification). We used Arabic-Bert in combination with data augmentation and ensembling methods. Unlabeled data provided by task organizers (10 Million tweets) was split into multiple subparts, to which we applied semi-supervised learning method, and finally ran a specific ensembling process on the resulting models. This system ranked 3rd in Subtask 1 with 23.26% F1-score and 2nd in Subtask 2 with 5.75% F1-score.- Anthology ID:
- 2020.wanlp-1.28
- Volume:
- Proceedings of the Fifth Arabic Natural Language Processing Workshop
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Imed Zitouni, Muhammad Abdul-Mageed, Houda Bouamor, Fethi Bougares, Mahmoud El-Haj, Nadi Tomeh, Wajdi Zaghouani
- Venue:
- WANLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 275–281
- Language:
- URL:
- https://aclanthology.org/2020.wanlp-1.28
- DOI:
- Cite (ACL):
- Kamel Gaanoun and Imade Benelallam. 2020. Arabic dialect identification: An Arabic-BERT model with data augmentation and ensembling strategy. In Proceedings of the Fifth Arabic Natural Language Processing Workshop, pages 275–281, Barcelona, Spain (Online). Association for Computational Linguistics.
- Cite (Informal):
- Arabic dialect identification: An Arabic-BERT model with data augmentation and ensembling strategy (Gaanoun & Benelallam, WANLP 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.wanlp-1.28.pdf