Dania Refai
Author directory2026
Bulbul: A Dataset for Dialectal Arabic Speech Recognition
Ahmed Ashraf | Aisha Alansari | Fadel Al Abbas | Nada Almarwani | Samah Aloufi | Saad Ezzini | Maged S. Al-shaibani | Doaa Dalaq | AbdelRahim A. Elmadany | Muhammad Abdul-Mageed | Mohamed Mehdi Trigui | Dania Refai | Layan Refai | Mohamed Akrout | Mustafa Jarrar | Wasfi G. Al-Khatib | Alaa Dalaq | Darin El-Nakla | Samir Abdaljalil | Abdulrahman Al-Fakih | Nour El Imane Zeghib | Moussa Redah | Salmane Chafik | Mohamed El-Attar | Rima Grati | Sarah Kohail | Malak Alkhorasani | Khadijah Al Safwan | Ismail Mohmmed Mudhaffar | Ali Altam | Ahmed Al-Shaikh | Adnan Mahyoub Ahmed Saeed | Hamzah Luqman
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Ahmed Ashraf | Aisha Alansari | Fadel Al Abbas | Nada Almarwani | Samah Aloufi | Saad Ezzini | Maged S. Al-shaibani | Doaa Dalaq | AbdelRahim A. Elmadany | Muhammad Abdul-Mageed | Mohamed Mehdi Trigui | Dania Refai | Layan Refai | Mohamed Akrout | Mustafa Jarrar | Wasfi G. Al-Khatib | Alaa Dalaq | Darin El-Nakla | Samir Abdaljalil | Abdulrahman Al-Fakih | Nour El Imane Zeghib | Moussa Redah | Salmane Chafik | Mohamed El-Attar | Rima Grati | Sarah Kohail | Malak Alkhorasani | Khadijah Al Safwan | Ismail Mohmmed Mudhaffar | Ali Altam | Ahmed Al-Shaikh | Adnan Mahyoub Ahmed Saeed | Hamzah Luqman
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Arabic automatic speech recognition (ASR) faces unique challenges due to diglossia, extensive regional dialect variation, and limited systematically balanced speech resources. Existing speech datasets often focus on single dialects or large-scale broadcast/web data, leading to trade-offs between linguistic diversity and annotation quality. We present BULBUL, a multi-dialect Arabic ASR dataset collected from 275 speakers across 11 Arab countries. BULBUL includes structured dialect and sub-dialect coverage, as well as recordings of classical Arabic and modern standard Arabic spoken by participants in their native dialectal accents to support accent-aware modeling. The quality of the recordings was ensured through a two-level human verification process. We further benchmark a range of recent ASR systems, establishing strong baselines for modern dialectal and accented Arabic ASR.
PromptLab: A Collaborative Platform for Prompt Engineering and Dataset Curation
Maged S. Al-shaibani | Zaid Alyafeai | Dania Refai | Nawaf Alomari | Ahmed Ashraf | Mais Alheraki | Mustafa Alturki | Hamzah Luqman | Irfan Ahmad
Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations)
Maged S. Al-shaibani | Zaid Alyafeai | Dania Refai | Nawaf Alomari | Ahmed Ashraf | Mais Alheraki | Mustafa Alturki | Hamzah Luqman | Irfan Ahmad
Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations)
PromptLab is a web-based platform for collaborative prompt engineering across diverse natural language processing tasks and datasets. The platform addresses primary challenges in prompt development, including template creation, collaborative review, and quality assurance through a comprehensive workflow that supports both individual researchers and team-based projects. PromptLab integrates with HuggingFace and provides AI-assisted prompt generation via OpenRouter[https://openrouter.ai/], and supporting real-time validation with multiple Large Language Models (LLMs). The platform features a flexible templating system using Jinja2, role-based project management, peer review processes, and supports programmatic access through RESTful APIs. To ensure data privacy and support sensitive research environments, PromptLab includes an easy CI/CD pipeline for self-hosted deployments and institutional control. We demonstrate the platform’s effectiveness through two evaluations: a controlled comparison study with six researchers across five benchmark datasets and 13 models with 90 prompts; and a comprehensive case study in instruction tuning research, where over 350 prompts across 80+ datasets have been developed and validated by multiple team members. The platform is available at https://promptlab.up.railway.app and the source code is available on GitHub at https://github.com/KFUPM-JRCAI/PromptLab.
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Co-authors
- Maged S. Al-shaibani 2
- Ahmed Ashraf 2
- Hamzah Luqman 2
- Samir Abdaljalil 1
- Muhammad Abdul-Mageed 1
- Irfan Ahmad 1
- Mohamed Akrout 1
- Fadel Al Abbas 1
- Khadijah Al Safwan 1
- Abdulrahman Al-Fakih 1
- Wasfi G. Al-Khatib 1
- Ahmed Al-Shaikh 1
- Aisha Alansari 1
- Mais Alheraki 1
- Malak Alkhorasani 1
- Nada Almarwani 1
- Nawaf Alomari 1
- Samah Aloufi 1
- Ali Altam 1
- Mustafa Alturki 1
- Zaid Alyafeai 1
- Salmane Chafik 1
- Alaa Dalaq 1
- Doaa Dalaq 1
- Mohamed El-Attar 1
- Darin El-Nakla 1
- AbdelRahim A. Elmadany 1
- Saad Ezzini 1
- Rima Grati 1
- Mustafa Jarrar 1
- Sarah Kohail 1
- Ismail Mohmmed Mudhaffar 1
- Moussa Redah 1
- Layan Refai 1
- Adnan Mahyoub Ahmed Saeed 1
- Mohamed Mehdi Trigui 1
- Nour El Imane Zeghib 1