Papers by Mohamad Ballout
Alexandria: A Multi-Domain Dialectal Arabic Machine Translation Dataset for Culturally Inclusive and Linguistically Diverse LLMs (2026.acl-long)
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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
| Challenge: | Arabic is a highly diglossic language where most daily communication occurs in regional dialects rather than modern standard Arabic (MSA). |
| Approach: | They propose a large-scale, community-driven, human-translated dataset to bridge this gap . Alexandria covers 13 Arab countries and 11 high-impact domains . it provides unprecedented granularity by associating contributions with city-of-origin metadata . |
| Outcome: | The Alexandria dataset covers 13 Arab countries and 11 high-impact domains . it provides unprecedented granularity by associating contributions with city-of-origin metadata . Alexandria is a training resource and a rigorous benchmark for evaluating MT and LLMs based on the Alexandria dataset . |
NeoAraBERT: A Modern Foundation Model for Arabic Embeddings with Diacritics-Aware Tokenization and POS-Targeted Masking (2026.findings-acl)
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Chadi Abou Chakra, Hadi Khaled Hamoud, Osama Rakan Al Mraikhat, Qusai Abu Obaida, Mohamad Ballout, Fadi Zaraket
| Challenge: | NeoAraBERT is an open-source text-embedding model for Arabic. |
| Approach: | They propose to train Arabic text-embedding models on open-source datasets . they benchmarked NeoAraBERT against five top-performing Arabic models on 23 tasks . |
| Outcome: | The proposed model outperforms five other models on 23 tasks in Arabic . it shows substantial improvement on classical and modern standard Arabic compared to other models . |
Can you SPLICE it together? A Human Curated Benchmark for Probing Visual Reasoning in VLMs (2025.findings-emnlp)
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Mohamad Ballout, Okajevo Wilfred, Seyedalireza Yaghoubi, Nohayr Muhammad Abdelmoneim, Julius Mayer, Elia Bruni
| Challenge: | SPLICE is a benchmark designed to probe event-based reasoning across multiple dimensions. |
| Approach: | They introduce a human-curated benchmark to probe event-based reasoning across multiple dimensions. |
| Outcome: | The proposed benchmark includes 3,381 human-filtered videos spanning 12 categories and 180 sub-categories . results show that state-of-the-art vision-language models struggle to match human performance . |
FOOL ME IF YOU CAN! An Adversarial Dataset to Investigate the Robustness of LMs in Word Sense Disambiguation (2024.emnlp-main)
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Mohamad Ballout, Anne Dedert, Nohayr Abdelmoneim, Ulf Krumnack, Gunther Heidemann, Kai-Uwe Kühnberger
| Challenge: | Word sense disambiguation (WSD) is a key task in natural language processing . however, these models struggle with recognizing semantic boundaries in adversarial contexts . |
| Approach: | They propose to use a coarse-grained WSD dataset to assess model robustness . they found that some models struggled to correctly disambiguate homonyms in adversarial contexts . |
| Outcome: | The proposed dataset includes four test sets to assess the robustness of language models in WSD tasks. |
iVISPAR — An Interactive Visual-Spatial Reasoning Benchmark for VLMs (2025.emnlp-main)
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| Challenge: | Vision-Language Models (VLMs) struggle with spatial reasoning and visual alignment, despite their performance on 2D tasks. |
| Approach: | They propose a multimodal benchmark to evaluate VLMs' spatial reasoning capabilities based on the sliding tile puzzle . |
| Outcome: | The proposed model performs better on 2D tasks compared to 3D or text-based settings, but struggles with complex spatial configurations and consistently falls short of human performance. |