Arabic Dialect Identification under Scrutiny: Limitations of Single-label Classification

Amr Keleg, Walid Magdy


Abstract
Automatic Arabic Dialect Identification (ADI) of text has gained great popularity since it was introduced in the early 2010s. Multiple datasets were developed, and yearly shared tasks have been running since 2018. However, ADI systems are reported to fail in distinguishing between the micro-dialects of Arabic. We argue that the currently adopted framing of the ADI task as a single-label classification problem is one of the main reasons for that. We highlight the limitation of the incompleteness of the Dialect labels and demonstrate how it impacts the evaluation of ADI systems. A manual error analysis for the predictions of an ADI, performed by 7 native speakers of different Arabic dialects, revealed that 67% of the validated errors are not true errors. Consequently, we propose framing ADI as a multi-label classification task and give recommendations for designing new ADI datasets.
Anthology ID:
2023.arabicnlp-1.31
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:
385–398
Language:
URL:
https://aclanthology.org/2023.arabicnlp-1.31
DOI:
10.18653/v1/2023.arabicnlp-1.31
Bibkey:
Cite (ACL):
Amr Keleg and Walid Magdy. 2023. Arabic Dialect Identification under Scrutiny: Limitations of Single-label Classification. In Proceedings of ArabicNLP 2023, pages 385–398, Singapore (Hybrid). Association for Computational Linguistics.
Cite (Informal):
Arabic Dialect Identification under Scrutiny: Limitations of Single-label Classification (Keleg & Magdy, ArabicNLP-WS 2023)
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PDF:
https://aclanthology.org/2023.arabicnlp-1.31.pdf