@inproceedings{mankarious-zirikly-2026-salm,
title = "{SALM}: Social {A}rabic Language Model",
author = "Mankarious, Saad and
Zirikly, Aya",
editor = "Abdul-Mageed, Muhammad and
Elsayed, Tamer and
Bouamor, Houda and
El-Beltagy, Samhaa and
Al-Khalifa, Hend and
Abdelali, Ahmed and
Elmadany, AbdelRahim and
El Mekki, Abdellah and
Darwish, Kareem and
Alkhereyf, Sakhar and
Luqman, Hamzah and
Khalifa, Salam and
Magdy, Samar and
Alyafeai, Zaid and
Hamed, Injy and
Zaghouani, Wajdi and
Zitouni, Imed",
booktitle = "Proceedings of The Fourth {A}rabic Natural Language Processing Conference",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.arabicnlp-main.34/",
pages = "497--507",
ISBN = "979-8-89176-473-6",
abstract = "Arabic NLP faces a fundamental mismatch between training and deployment conditions. While most language models are trained predominantly on Modern Standard Arabic (MSA), real-world communication occurs largely in spoken dialects. To bridge this gap, we leverage social media content from Reddit, Twitter, and YouTube as a scalable proxy for spoken Arabic. We construct a 1.04-billion-token pretraining corpus and train SALM, a compact 336M-parameter decoder-only language model pre-trained via next-token prediction alone, no masked language modelling, no dialect labels, no task-specific signal. Despite an order-of-magnitude smaller training corpus than existing dialectal PLMs and a generative training objective that provides no direct classification signal, SALM matches MARBERT, explicitly pre-trained on one billion dialectal tweets, within 0.7 percentage points on MADAR-26 dialect identification accuracy and outperforms it on macro-F1. Under full fine-tuning, SALM surpasses all baselines, achieving 69.1{\%} accuracy on 26-way city-level dialect classification. Under the same frozen probe on binary Arabic sentiment (AJGT), SALM achieves 92.2{\%} accuracy, topping all Arabic PLMs, encoder and decoder alike. We release the corpus and trained model to facilitate future research on Arabic spoken language modelling."
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<abstract>Arabic NLP faces a fundamental mismatch between training and deployment conditions. While most language models are trained predominantly on Modern Standard Arabic (MSA), real-world communication occurs largely in spoken dialects. To bridge this gap, we leverage social media content from Reddit, Twitter, and YouTube as a scalable proxy for spoken Arabic. We construct a 1.04-billion-token pretraining corpus and train SALM, a compact 336M-parameter decoder-only language model pre-trained via next-token prediction alone, no masked language modelling, no dialect labels, no task-specific signal. Despite an order-of-magnitude smaller training corpus than existing dialectal PLMs and a generative training objective that provides no direct classification signal, SALM matches MARBERT, explicitly pre-trained on one billion dialectal tweets, within 0.7 percentage points on MADAR-26 dialect identification accuracy and outperforms it on macro-F1. Under full fine-tuning, SALM surpasses all baselines, achieving 69.1% accuracy on 26-way city-level dialect classification. Under the same frozen probe on binary Arabic sentiment (AJGT), SALM achieves 92.2% accuracy, topping all Arabic PLMs, encoder and decoder alike. We release the corpus and trained model to facilitate future research on Arabic spoken language modelling.</abstract>
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%0 Conference Proceedings
%T SALM: Social Arabic Language Model
%A Mankarious, Saad
%A Zirikly, Aya
%Y Abdul-Mageed, Muhammad
%Y Elsayed, Tamer
%Y Bouamor, Houda
%Y El-Beltagy, Samhaa
%Y Al-Khalifa, Hend
%Y Abdelali, Ahmed
%Y Elmadany, AbdelRahim
%Y El Mekki, Abdellah
%Y Darwish, Kareem
%Y Alkhereyf, Sakhar
%Y Luqman, Hamzah
%Y Khalifa, Salam
%Y Magdy, Samar
%Y Alyafeai, Zaid
%Y Hamed, Injy
%Y Zaghouani, Wajdi
%Y Zitouni, Imed
%S Proceedings of The Fourth Arabic Natural Language Processing Conference
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-473-6
%F mankarious-zirikly-2026-salm
%X Arabic NLP faces a fundamental mismatch between training and deployment conditions. While most language models are trained predominantly on Modern Standard Arabic (MSA), real-world communication occurs largely in spoken dialects. To bridge this gap, we leverage social media content from Reddit, Twitter, and YouTube as a scalable proxy for spoken Arabic. We construct a 1.04-billion-token pretraining corpus and train SALM, a compact 336M-parameter decoder-only language model pre-trained via next-token prediction alone, no masked language modelling, no dialect labels, no task-specific signal. Despite an order-of-magnitude smaller training corpus than existing dialectal PLMs and a generative training objective that provides no direct classification signal, SALM matches MARBERT, explicitly pre-trained on one billion dialectal tweets, within 0.7 percentage points on MADAR-26 dialect identification accuracy and outperforms it on macro-F1. Under full fine-tuning, SALM surpasses all baselines, achieving 69.1% accuracy on 26-way city-level dialect classification. Under the same frozen probe on binary Arabic sentiment (AJGT), SALM achieves 92.2% accuracy, topping all Arabic PLMs, encoder and decoder alike. We release the corpus and trained model to facilitate future research on Arabic spoken language modelling.
%U https://aclanthology.org/2026.arabicnlp-main.34/
%P 497-507
Markdown (Informal)
[SALM: Social Arabic Language Model](https://aclanthology.org/2026.arabicnlp-main.34/) (Mankarious & Zirikly, ArabicNLP 2026)
ACL
- Saad Mankarious and Aya Zirikly. 2026. SALM: Social Arabic Language Model. In Proceedings of The Fourth Arabic Natural Language Processing Conference, pages 497–507, Budapest, Hungary. Association for Computational Linguistics.