# Revisiting Common Assumptions about Arabic Dialects in NLP

arXiv: https://arxiv.org/abs/2505.21816 · HTML: https://mlsanity.com/paper/2505.21816/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 88 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 21 of 21)

- [Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English](https://mlsanity.com/paper/1707.00061/) (2017)
- [We Need to Talk About Random Splits](https://mlsanity.com/paper/2005.00636/) (2020)
- [NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2010.11334/) (2020)
- [Arabic Dialect Identification in the Wild](https://mlsanity.com/paper/2005.06557/) (2020)
- [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)
- [Arap-Tweet: A Large Multi-Dialect Twitter Corpus for Gender, Age and Language Variety Identification](https://mlsanity.com/paper/1808.07674/) (2018)
- [NADI 2022: The Third Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2210.09582/) (2022)
- [NADI 2024: The Fifth Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2407.04910/) (2024)
- [Multi-Dialect Arabic BERT for Country-Level Dialect Identification](https://mlsanity.com/paper/2007.05612/) (2020)
- [Towards Responsible Natural Language Annotation for the Varieties of Arabic](https://mlsanity.com/paper/2203.09597/) (2022)
- [Arabic Dialect Identification under Scrutiny: Limitations of Single-label Classification](https://mlsanity.com/paper/2310.13661/) (2023)
- [Language Variety Identification with True Labels](https://mlsanity.com/paper/2303.01490/) (2023)
- [AL-QASIDA: Analyzing LLM Quality and Accuracy Systematically in Dialectal Arabic](https://mlsanity.com/paper/2412.04193/) (2024)
- [Automatic Arabic Dialect Identification Systems for Written Texts: A Survey](https://mlsanity.com/paper/2009.12622/) (2020)
- [BERT-based Multi-Task Model for Country and Province Level Modern Standard Arabic and Dialectal Arabic Identification](https://mlsanity.com/paper/2106.12495/) (2021)
- [Adapting MARBERT for Improved Arabic Dialect Identification: Submission to the NADI 2021 Shared Task](https://mlsanity.com/paper/2103.01065/) (2021)
- [Estimating the Level of Dialectness Predicts Interannotator Agreement in Multi-dialect Arabic Datasets](https://mlsanity.com/paper/2405.11282/) (2024)
- [Common Ground, Diverse Roots: The Difficulty of Classifying Common Examples in Spanish Varieties](https://mlsanity.com/paper/2412.11750/) (2024)
- [TunBERT: Pretrained Contextualized Text Representation for Tunisian Dialect](https://mlsanity.com/paper/2111.13138/) (2021)

## Built on by (most cited first, 2 of 2)

- [Evaluating Arabic Large Language Models: A Survey of Benchmarks, Methods, and Gaps](https://mlsanity.com/paper/2510.13430/) (2025)
- [Romanized Arabic Across Dialects: Views, Usage Patterns, and Linguistic Variation](https://mlsanity.com/paper/2608.02555/) (2026)

## Abstract (arXiv)

Arabic has diverse dialects, where one dialect can be substantially different from the others. In the NLP literature, some assumptions about these dialects are widely adopted (e.g., ``Arabic dialects can be grouped into distinguishable regional dialects") and are manifested in different computational tasks such as Arabic Dialect Identification (ADI). However, these assumptions are not quantitatively verified. We identify four of these assumptions and examine them by extending and analyzing a multi-label dataset, where the validity of each sentence in 11 different country-level dialects is manually assessed by speakers of these dialects. Our analysis indicates that the four assumptions oversimplify reality, and some of them are not always accurate. This in turn might be hindering further progress in different Arabic NLP tasks.