Abstract
In this paper, we present our findings in the two subtasks of the 2022 NADI shared task. First, in the Arabic dialect identification subtask, we find that there is heavy class imbalance, and propose to address this issue using focal loss. Our experiments with the focusing hyperparameter confirm that focal loss improves performance. Second, in the Arabic tweet sentiment analysis subtask, we deal with a smaller dataset, where text includes both Arabic dialects and Modern Standard Arabic. We propose to use transfer learning from both pre-trained MSA language models and our own model from the first subtask. Our system ranks in the 5th and 7th best spots of the leaderboards of first and second subtasks respectively.- Anthology ID:
- 2022.wanlp-1.46
- 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:
- 442–446
- Language:
- URL:
- https://aclanthology.org/2022.wanlp-1.46
- DOI:
- 10.18653/v1/2022.wanlp-1.46
- Cite (ACL):
- Ahmed Oumar and Khalil Mrini. 2022. Ahmed and Khalil at NADI 2022: Transfer Learning and Addressing Class Imbalance for Arabic Dialect Identification and Sentiment Analysis. In Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP), pages 442–446, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Ahmed and Khalil at NADI 2022: Transfer Learning and Addressing Class Imbalance for Arabic Dialect Identification and Sentiment Analysis (Oumar & Mrini, WANLP 2022)
- PDF:
- https://preview.aclanthology.org/landing_page/2022.wanlp-1.46.pdf