Kareem Darwish, Hamdy Mubarak, Ahmed Abdelali, Mohamed Eldesouki, Younes Samih, Randah Alharbi, Mohammed Attia, Walid Magdy, Laura Kallmeyer
| Challenge: | Existing work on dialectal POS tagging is rather scant with POS tags for most dialects being nonexistent or of limited availability. |
| Approach: | They propose a dataset of POS-tagged Arabic tweets in four major dialects and a tagging guideline for each dialect. |
| Outcome: | The proposed model can tag four different dialects with an average accuracy of 89.3%. |
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| Challenge: | The Arabic language is the fifth most widely spoken language in the world; more than 380 million people speak and write in Arabic. |
| Approach: | They propose to build a large manually-annotated multi-dialect dataset of Arabic tweets that is publicly available. |
| Outcome: | The proposed dataset is well-balanced over five main Arabic dialects: Egyptian, Maghrebi, Levantine, Gulf, and Iraqi. |
You Tweet What You Speak: A City-Level Dataset of Arabic Dialects (L18-1)
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| Challenge: | Existing studies of Arabic dialects have focused on blogs and comments on online news sites, but data on other dialects are costly and limited. |
| Approach: | They present a dataset of > 1/4 billion tweets representing a wide range of Arabic dialects. |
| Outcome: | The dataset represents 29 major Arab cities from 10 Arab countries with varying dialects. |
Part-of-Speech Tagging for Arabic Gulf Dialect Using Bi-LSTM (L18-1)
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| Challenge: | Part-of-speech (POS) tagging is one of the most important building blocks in many natural language processing (NLP) applications. |
| Approach: | They propose to use a POS tagger for Arabic Gulf dialect to improve POS tagging accuracy. |
| Outcome: | The proposed POS tagger improves POS tagging accuracy for the Arabic Gulf dialect from 75% accuracy to 91% accuracy using a bi-LSTM labeler. |
Active Learning for Multidialectal Arabic POS Tagging (2025.findings-emnlp)
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| Challenge: | Multidialectal Arabic POS tagging is challenging due to the morphological richness and high variability among dialects. |
| Approach: | They propose an active learning approach for multidialectal Arabic POS tagging . they annotate approximately 15,000 tokens, reducing the annotation requirement by about 2,000 tokens . |
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Arap-Tweet: A Large Multi-Dialect Twitter Corpus for Gender, Age and Language Variety Identification (L18-1)
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| Challenge: | Existing corpus of Arabic textual data is limited to English or other European languages. |
| Approach: | They present a large-scale and multi-dialectal corpus of Tweets from 11 regions and 16 countries in the arab world representing the major Arabic dialectal varieties. |
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Morphosyntactic Tagging with Pre-trained Language Models for Arabic and its Dialects (2022.findings-acl)
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| Challenge: | Pre-trained morphosyntactic tagging models outperform existing systems in Modern Standard Arabic and all the Arabic dialects studied. |
| Approach: | They present results on morphosyntactic tagging across different varieties of Arabic using pre-trained transformer language models. |
| Outcome: | The proposed models outperform existing systems in Modern Standard Arabic, 2.8% in Gulf, 1.6% in Egyptian, and 8.3% in Levantine. |
Revisiting Common Assumptions about Arabic Dialects in NLP (2025.acl-long)
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| Challenge: | Existing assumptions about Arabic dialect variation are not quantitatively verified. |
| Approach: | They extend and analyze Arabic dialects to assess their validity using a multi-label dataset . they find that the assumptions oversimplify reality and are not always accurate . |
| Outcome: | The proposed methods oversimplify reality and are not always accurate, the authors argue . they show that the proposed assumptions oversimply represent reality and may hinder future work . |
Alexandria: A Multi-Domain Dialectal Arabic Machine Translation Dataset for Culturally Inclusive and Linguistically Diverse LLMs (2026.acl-long)
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Abdellah EL Mekki, Samar M. Magdy, Houdaifa Atou, Ruwa AbuHweidi, Baraah Qawasmeh, Omer Nacar, Thikra Al-hibiri, Razan Saadie, Hamzah A. Alsayadi, Nadia Ghezaiel Hammouda, Alshima Mohammed Alkhazimi, Aya Hamod, Al-Yas Yaqoob Al-Ghafri, Wesam El-Sayed, Asila Ismail al Sharji, Mohamad Ballout, Anas Belfathi, Karim Ghaddar, Serry Sibaee, Alaa Aoun, Aeej Mohammed Aseri, Lina Abureesh, Ahlam Bashiti, Majdal Yousef, Abdulaziz Hafiz, Yehdih Mohamed, Emira Hamedtou, Brakehe Emehah, Rahaf Alhamouri, Youssef Nafea, Aya El Aatar, Walid Al-Dhabyani, Emhemed S. Hamed, Sara Shatnawi, Fakhraddin Alwajih, Khalid Elkhidir, Ashwag Alasmari, Abdurrahman Gerrio, Omar Said Alshahri, AbdelRahim A. Elmadany, Ismail Berrada, Amir Azad Adli Al-kathiri, Fadi Zaraket, Mustafa Jarrar, Yahya Mohamed EL Hadj, Hassan Alhuzali, Muhammad Abdul-Mageed
| Challenge: | Arabic is a highly diglossic language where most daily communication occurs in regional dialects rather than modern standard Arabic (MSA). |
| Approach: | They propose a large-scale, community-driven, human-translated dataset to bridge this gap . Alexandria covers 13 Arab countries and 11 high-impact domains . it provides unprecedented granularity by associating contributions with city-of-origin metadata . |
| Outcome: | The Alexandria dataset covers 13 Arab countries and 11 high-impact domains . it provides unprecedented granularity by associating contributions with city-of-origin metadata . Alexandria is a training resource and a rigorous benchmark for evaluating MT and LLMs based on the Alexandria dataset . |
Arab Voices: Mapping Standard and Dialectal Arabic Speech Technology (2026.findings-acl)
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| Challenge: | Dialectal Arabic datasets embody a range of domain, dialect, and quality. |
| Approach: | They propose a framework for automatic speech recognition in dialectal Arabic to address the limited data availability encountered in dialects. |
| Outcome: | The proposed framework provides access to 31 datasets covering 14 dialects to better address the limited data availability encountered in dialectal Arabic speech processing. |
Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling (2021.eacl-main)
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| Challenge: | Existing approaches to fine-tune pre-trained language models for downstream tasks require labeled data. |
| Approach: | They propose to self-train pre-trained language models to improve performance on data-scarce varieties by as large as 10% F1 and 2% accuracy. |
| Outcome: | The proposed model improves zero-shot MSA-to-DA transfer by as large as 10% F1 (NER) and 2% accuracy (POS tagging). |