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20242026
most citedCasablanca: Data and Models for Multidialectal Arabic Speech Recognition

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cs.CL2026

LQM: Linguistically Motivated Multidimensional Quality Metrics for Machine Translation

Samar M. Magdy, Fakhraddin Alwajih, Abdellah El Mekki +2

Existing MT evaluation frameworks, including automatic metrics and human evaluation schemes such as Multidimensional Quality Metrics (MQM), are largely language-agnostic. However,…

cs.CL2026

Alexandria: A Multi-Domain Dialectal Arabic Machine Translation Dataset for Culturally Inclusive and Linguistically Diverse LLMs

Abdellah El Mekki, Samar M. Magdy, Houdaifa Atou +44

Arabic is a highly diglossic language where most daily communication occurs in regional dialects rather than Modern Standard Arabic (MSA). Despite this, machine translation (MT) sy…

cs.CL2025

Are LLMs Good Text Diacritizers? An Arabic and Yoruba Case Study

Hawau Olamide Toyin, Samar Mohamed Magdy, Hanan Aldarmaki

We investigate the effectiveness of large language models (LLMs) for text diacritization in two typologically distinct languages: Arabic and Yoruba. To enable a rigorous evaluation…

cs.CL2025

ArVoice: A Multi-Speaker Dataset for Arabic Speech Synthesis

Hawau Olamide Toyin, Rufael Marew, Humaid Alblooshi +2

We introduce ArVoice, a multi-speaker Modern Standard Arabic (MSA) speech corpus with diacritized transcriptions, intended for multi-speaker speech synthesis, and can be useful for…

cs.CL2025

Pearl: A Multimodal Culturally-Aware Arabic Instruction Dataset

Fakhraddin Alwajih, Samar M. Magdy, Abdellah El Mekki +34

Mainstream large vision-language models (LVLMs) inherently encode cultural biases, highlighting the need for diverse multimodal datasets. To address this gap, we introduce PEARL, a…

cs.CL2025

Palm: A Culturally Inclusive and Linguistically Diverse Dataset for Arabic LLMs

Fakhraddin Alwajih, Abdellah El Mekki, Samar Mohamed Magdy +41

As large language models (LLMs) become increasingly integrated into daily life, ensuring their cultural sensitivity and inclusivity is paramount. We introduce our dataset, a year-l…