Model Card for ADI-NADI-2023
A BERT-based model fine-tuned to perform single-label Arabic Dialect Identification (ADI). The model was used in the following paper: Arabic Dialect Identification under Scrutiny: Limitations of Single-label Classification
Model Description
- Model type: A Dialect Identification model fine-tuned on NADI 2023's training data.
- Language(s) (NLP): Arabic.
- Finetuned from model : MarBERT
Citation
If you find the model useful, please cite the following respective paper:
@inproceedings{keleg-magdy-2023-arabic,
title = "{A}rabic Dialect Identification under Scrutiny: Limitations of Single-label Classification",
author = "Keleg, Amr and
Magdy, Walid",
editor = "Sawaf, Hassan and
El-Beltagy, Samhaa and
Zaghouani, Wajdi and
Magdy, Walid and
Abdelali, Ahmed and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Habash, Nizar and
Khalifa, Salam and
Keleg, Amr and
Haddad, Hatem and
Zitouni, Imed and
Mrini, Khalil and
Almatham, Rawan",
booktitle = "Proceedings of ArabicNLP 2023",
month = dec,
year = "2023",
address = "Singapore (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.arabicnlp-1.31",
doi = "10.18653/v1/2023.arabicnlp-1.31",
pages = "385--398",
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 $\approx$ 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.",
}