Papers with Arabic-speaking countries
JEEM: Vision-Language Understanding in Four Arabic Dialects (2026.findings-eacl)
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Karima Kadaoui, Hanin Atwany, Hamdan Al-Ali, Abdelrahman Mohamed, Ali Mekky, Sergei Tilga, Natalia Fedorova, Ekaterina Artemova, Hanan Aldarmaki, Yova Kementchedjhieva
| Challenge: | Existing evaluation datasets feature Western-centric images and English text, while their non-English counterparts are often derived from the latter. |
| Approach: | They propose to evaluate Vision-Language Models (VLMs) on visual understanding across four Arabic-speaking countries: Jordan, The Emirates, Egypt, and Morocco. |
| Outcome: | The proposed model underperforms in visual understanding and dialect-specific generation across four Arabic-speaking countries. |
Cultural Benchmarking of LLMs in Standard and Dialectal Arabic Dialogues (2026.acl-long)
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Muhammad Dehan Al Kautsar, Saeed Almheiri, Momina Ahsan, Bilal Elbouardi, Younes Samih, Sarfraz Ahmad, Amr Keleg, Omar El Herraoui, Kareem Elzeky, Abed Alhakim Freihat, Mohamed Anwar, Zhuohan Xie, Junhong Liang, Mohammad Rustom Al Nasar, Preslav Nakov, Fajri Koto
| Challenge: | Most benchmarks focus on short text snippets in Modern Standard Arabic (MSA), overlooking cultural nuances that naturally arise in dialogues. |
| Approach: | They propose a culturally grounded conversational dataset covering 13 Arabic-speaking countries, in both Modern Standard Arabic (MSA) and each country’s respective dialect, spanning 12 daily-life topics and 54 fine-grained subtopics. |
| Outcome: | The proposed model performs worse on all three tasks than the MSA benchmark. |
Dialectal Coverage And Generalization in Arabic Speech Recognition (2025.acl-long)
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| Challenge: | Existing ASR systems cover the modern standard Arabic variety but fail to cover the multitude of spoken variants. |
| Approach: | They propose a suite of automatic speech recognition models optimized to recognize multiple variants of spoken Arabic. |
| Outcome: | The proposed models show coverage and performance gains compared to prior models. |