# Automatic Arabic Dialect Identification Systems for Written Texts: A Survey

arXiv: https://arxiv.org/abs/2009.12622 · HTML: https://mlsanity.com/paper/2009.12622/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2020.

## Built on by (most cited first, 10 of 10)

- [NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2103.08466/) (2021)
- [NADI 2023: The Fourth Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2310.16117/) (2023)
- [ALDi: Quantifying the Arabic Level of Dialectness of Text](https://mlsanity.com/paper/2310.13747/) (2023)
- [NADI 2022: The Third Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2210.09582/) (2022)
- [Arabic Dialect Identification under Scrutiny: Limitations of Single-label Classification](https://mlsanity.com/paper/2310.13661/) (2023)
- [Common Ground, Diverse Roots: The Difficulty of Classifying Common Examples in Spanish Varieties](https://mlsanity.com/paper/2412.11750/) (2024)
- [Revisiting Common Assumptions about Arabic Dialects in NLP](https://mlsanity.com/paper/2505.21816/) (2025)
- [Curriculum Learning and Pseudo-Labeling Improve the Generalization of Multi-Label Arabic Dialect Identification Models](https://mlsanity.com/paper/2602.12937/) (2026)
- [Ramsa: A Large Sociolinguistically Rich Emirati Arabic Speech Corpus for ASR and TTS](https://mlsanity.com/paper/2603.08125/) (2026)
- [Jointly Improving Dialect Identification and ASR in Indian Languages using Multimodal Feature Fusion](https://mlsanity.com/paper/2607.02862/) (2026)

## Abstract (arXiv)

Arabic dialect identification is a specific task of natural language processing, aiming to automatically predict the Arabic dialect of a given text. Arabic dialect identification is the first step in various natural language processing applications such as machine translation, multilingual text-to-speech synthesis, and cross-language text generation. Therefore, in the last decade, interest has increased in addressing the problem of Arabic dialect identification. In this paper, we present a comprehensive survey of Arabic dialect identification research in written texts. We first define the problem and its challenges. Then, the survey extensively discusses in a critical manner many aspects related to Arabic dialect identification task. So, we review the traditional machine learning methods, deep learning architectures, and complex learning approaches to Arabic dialect identification. We also detail the features and techniques for feature representations used to train the proposed systems. Moreover, we illustrate the taxonomy of Arabic dialects studied in the literature, the various levels of text processing at which Arabic dialect identification are conducted (e.g., token, sentence, and document level), as well as the available annotated resources, including evaluation benchmark corpora. Open challenges and issues are discussed at the end of the survey.