# Similarities between Arabic Dialects: Investigating Geographical Proximity

arXiv: https://arxiv.org/abs/2105.04221 · HTML: https://mlsanity.com/paper/2105.04221/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 53 references that are in the map.
- First posted on arXiv 10 May 2021.

## Builds on (most cited first, 6 of 6)

- [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)
- [A Panoramic Survey of Natural Language Processing in the Arab World](https://mlsanity.com/paper/2011.12631/) (2020)
- [NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2103.08466/) (2021)
- [Multi-Dialect Arabic BERT for Country-Level Dialect Identification](https://mlsanity.com/paper/2007.05612/) (2020)
- [Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments](https://mlsanity.com/paper/2010.04900/) (2020)

## Built on by (most cited first, 5 of 5)

- [NADI 2022: The Third Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2210.09582/) (2022)
- [Comparing Open Arabic Named Entity Recognition Tools](https://mlsanity.com/paper/2205.05857/) (2022)
- [From FusHa to Folk: Exploring Cross-Lingual Transfer in Arabic Language Models](https://mlsanity.com/paper/2602.09826/) (2026)
- [Normalized Orthography for Tunisian Arabic](https://mlsanity.com/paper/2402.12940/) (2024)
- [Text-to-Image Models and Their Representation of People from Different Nationalities Engaging in Activities](https://mlsanity.com/paper/2504.06313/) (2025)

## Abstract (arXiv)

The automatic classification of Arabic dialects is an ongoing research challenge, which has been explored in recent work that defines dialects based on increasingly limited geographic areas like cities and provinces. This paper focuses on a related yet relatively unexplored topic: the effects of the geographical proximity of cities located in Arab countries on their dialectical similarity. Our work is twofold, reliant on: 1) comparing the textual similarities between dialects using cosine similarity and 2) measuring the geographical distance between locations. We study MADAR and NADI, two established datasets with Arabic dialects from many cities and provinces. Our results indicate that cities located in different countries may in fact have more dialectical similarity than cities within the same country, depending on their geographical proximity. The correlation between dialectical similarity and city proximity suggests that cities that are closer together are more likely to share dialectical attributes, regardless of country borders. This nuance provides the potential for important advancements in Arabic dialect research because it indicates that a more granular approach to dialect classification is essential to understanding how to frame the problem of Arabic dialects identification.