Abstract
With approximately 400 million speakers worldwide, Arabic ranks as the fifth most-spoken language globally, necessitating advancements in natural language processing. This paper addresses this need by presenting a system description of the approaches employed for the subtasks outlined in the Nuanced Arabic Dialect Identification (NADI) task at EMNLP 2023. For the first subtask, involving closed country-level dialect identification classification, we employ an ensemble of two Arabic language models. Similarly, for the second subtask, focused on closed dialect to Modern Standard Arabic (MSA) machine translation, our approach combines sequence-to-sequence models, all trained on an Arabic-specific dataset. Our team ranks 10th and 3rd on subtask 1 and subtask 2 respectively.- Anthology ID:
- 2023.arabicnlp-1.63
- Volume:
- Proceedings of ArabicNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore (Hybrid)
- Editors:
- Hassan Sawaf, Samhaa El-Beltagy, Wajdi Zaghouani, Walid Magdy, Ahmed Abdelali, Nadi Tomeh, Ibrahim Abu Farha, Nizar Habash, Salam Khalifa, Amr Keleg, Hatem Haddad, Imed Zitouni, Khalil Mrini, Rawan Almatham
- Venues:
- ArabicNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 614–619
- Language:
- URL:
- https://aclanthology.org/2023.arabicnlp-1.63
- DOI:
- 10.18653/v1/2023.arabicnlp-1.63
- Cite (ACL):
- Hariram Veeramani, Surendrabikram Thapa, and Usman Naseem. 2023. DialectNLU at NADI 2023 Shared Task: Transformer Based Multitask Approach Jointly Integrating Dialect and Machine Translation Tasks in Arabic. In Proceedings of ArabicNLP 2023, pages 614–619, Singapore (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- DialectNLU at NADI 2023 Shared Task: Transformer Based Multitask Approach Jointly Integrating Dialect and Machine Translation Tasks in Arabic (Veeramani et al., ArabicNLP-WS 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.arabicnlp-1.63.pdf