Revisiting Common Assumptions about Arabic Dialects in NLP
Amr Keleg, Sharon Goldwater, Walid Magdy
Abstract
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.
BibTeX
@inproceedings{keleg-etal-2025-revisiting,
title = "Revisiting Common Assumptions about {A}rabic Dialects in {NLP}",
author = "Keleg, Amr and
Goldwater, Sharon and
Magdy, Walid",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.166/",
doi = "10.18653/v1/2025.acl-long.166",
pages = "3309--3327",
ISBN = "979-8-89176-251-0"
}