Papers by Mohamed Al-Badrashiny
Arabic Diacritization Using Morphologically Informed Character-Level Model (2024.lrec-main)
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Muhammad Morsy Elmallah, Mahmoud Reda, Kareem Darwish, Abdelrahman El-Sheikh, Ashraf Hatim Elneima, Murtadha Aljubran, Nouf Alsaeed, Reem Mohammed, Mohamed Al-Badrashiny
| Challenge: | Diacritics are typically omitted in Arabic writings and the reader needs to guess the proper diacritics as they are reading. |
| Approach: | They propose a morphologically informed character-level model that can recover both types of diacritics simultaneously. |
| Outcome: | The proposed model achieves lowest word-level diacritization error rate for Classical Arabic, MSA, and two dialectal Arabic texts. |
Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines (2025.acl-demo)
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| Challenge: | a single AI model is often insufficient for complex tasks, requiring integration of multiple models into pipelines . a conversational agent can build pipelines composed of diverse AI models based on user requirements . |
| Approach: | They propose a conversational agent that constructs AI model pipelines based on user requirements. |
| Outcome: | The proposed agent can build AI model pipelines from human-curated and synthetic data. |
Media-to-Insights: A Multi-Agent AI System for Continuous Media Monitoring, Analysis, and Reporting (2026.acl-demo)
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Ashraf Hatim Elneima, Ozan Yilmaz, Hadi Nasrallah, Sanjika Hewavitharana, Mohamed Al-Badrashiny, Hassan Sawaf
| Challenge: | Existing systems that use keyword-based media monitoring miss semantically relevant articles and are expensive at scale. |
| Approach: | They propose a multi-agent media monitoring system that processes streaming articles through three stages: article matching, batched feature extraction, and report generation with deterministic deduplication and density-based clustering. |
| Outcome: | The proposed system reduces agent invocations by 20% and reduces core feature extraction calls from 7 to 2 per article - a 71% reduction - with bounded quality tradeoffs . |
Agentic AI for Human Resources: LLM-Driven Candidate Assessment (2026.eacl-demo)
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Kamer Ali Yuksel, Abdul Basit Anees, Ashraf Hatim Elneima, Sanjika Hewavitharana, Mohamed Al-Badrashiny, Hassan Sawaf
| Challenge: | Current systems rely on keyword matching and shallow keyword-based screening, leading to missed opportunities and inconsistent evaluations. |
| Approach: | They propose a framework that uses Large Language Models to automate candidate assessment in recruitment. |
| Outcome: | The proposed framework outputs detailed assessment reports, candidate comparisons, and ranked recommendations that are transparent, auditable, and suitable for real-world hiring workflows. |
MTLens: Machine Translation Output Debugging (2022.lrec-1)
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Shreyas Sharma, Kareem Darwish, Lucas Pavanelli, Thiago Castro Ferreira, Mohamed Al-Badrashiny, Kamer Ali Yuksel, Hassan Sawaf
| Challenge: | a demo demonstrates a system for quantitatively evaluating MT systems in isolation or multiple MT models collectively . performance of machine translation systems varies significantly with inputs of diverging features, such as genres, genres and surface properties. |
| Approach: | They propose a benchmarking interface that quantitatively evaluates MT systems in isolation or collectively . the interface can be extended to include additional filters such as lexical, morphological, and syntactic features. |
| Outcome: | The proposed system quantitatively evaluates MT systems on multiple domains and evaluation metrics. |