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. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Emotion recognition plays an important role in enhancing social communications and is a key component of improving human-machine interactions. With the rise of deep learning, emotion recognition systems are becoming more effective and accurate. However, progress is often limited by the availability of datasets, a problem affecting low-resourced languages such as the Moroccan Arabic Dialect (Darija). This article introduces the Moroccan Arabic Multimodal Authentic Emotional Dataset (MORAD), a multimodal dataset collected to address the scarcity of affective datasets in the Moroccan Arabic Dialect. MORAD comprises 2325 videos, totaling 3 h, and includes 329 unique speakers expressing their emotions naturally in unscripted real-world settings. The dataset contains seven emotions: 1) happiness; 2) sadness; 3) anger; 4) fear; 5) disgust; 6) surprise; and 7) neutrality. We evaluated various deep learning models on this dataset, including unimodal speech, visual, and textual models, as well as a multimodal approach. The multimodal model achieved the highest performance with a weighted F1 score of 73.11% and an accuracy of 73.36% on a 4-way classification task, and 66.69% and 69.38%, respectively, on the 7-way task. These results provide a benchmark demonstrating MORAD’s utility for emotion recognition.

Fakhraddin Alwajih, Abdellah El Mekki, Samar Mohamed Magdy, AbdelRahim A. Elmadany, Omer Nacar, El Moatez Billah Nagoudi, Reem Abdel-Salam, Hanin Atwany, Youssef Nafea, Abdulfattah Mohammed Yahya, Rahaf Alhamouri, Hamzah A. Alsayadi, Hiba Zayed, Sara Shatnawi, Serry Sibaee, Yasir Ech-chammakhy, Walid Al-Dhabyani, Marwa Mohamed Ali, Imen Jarraya, Ahmed Oumar El-Shangiti, Aisha Alraeesi, Mohammed Anwar AL-Ghrawi, Abdulrahman S. Al-Batati, Elgizouli Mohamed, Noha Taha Elgindi, Muhammed Saeed, Houdaifa Atou, Issam Ait Yahia, Abdelhak Bouayad, Mohammed Machrouh, Amal Makouar, Dania Alkawi, Mukhtar Mohamed, Safaa Taher Abdelfadil, Amine Ziad Ounnoughene, Anfel Rouabhia, Rwaa Assi, Ahmed Sorkatti, Mohamedou Cheikh Tourad, Anis Koubaa, Ismail Berrada, Mustafa Jarrar, Shady Shehata, Muhammad Abdul-Mageed. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Translate to low-resource language LLM‫ھﺗﺎﻛل؟‬ ‫ﻣش‬ ‫ﻣﺻطﻔﻰ،‬ ‫ﯾﺎ‬ ‫إزﯾك‬ ‫أﺣﻣد:‬ ‫ده،‬ ‫اﻟﺷﺎرع‬ ‫أﻛل‬ ‫ﻣن‬ ‫ﺑﻘﻠﻖ‬ ‫اﻟﻣدام.‬‫ﺑﻛﻠم‬ ‫ﻛﻧت‬ ‫ﻣﻌﻠش،‬ ‫ﻣﺻطﻔﻰ: