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.",
}