Papers by Ahmed Abdelali
The WAW Corpus: The First Corpus of Interpreted Speeches and their Translations for English and Arabic (L18-1)
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| Challenge: | Using the corpus, we study the characteristics of interpreters' work and train machine translation systems. |
| Approach: | They propose to build an interpreting corpus for Arabic and an Arabic corpus to study interpreters' work. |
| Outcome: | The proposed corpus can be used for teaching interpreters and to train machine translation systems. |
ASAD: Arabic Social media Analytics and unDerstanding (2021.eacl-demos)
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| Challenge: | Currently, there are no publicly available tools for analyzing Arabic social media, such as ADIDA and CAMeL, which are not trained with Twitter data. |
| Approach: | They propose to use Arabic social media analysis and unDerstanding to analyze tweets using a web API and a user interface. |
| Outcome: | The proposed system allows users to determine dialects, sentiment, news category, offensiveness, hate speech, adult content, and spam in Arabic tweets. |
Exploring Alignment in Shared Cross-lingual Spaces (2024.acl-long)
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| Challenge: | a new study examines the degree of alignment between languages in multilingual embeddings . cross-lingual embeds are designed to encode linguistic concepts that bridge equivalent semantic meaning . a comprehensive approach is needed to address these questions. |
| Approach: | They employ clustering to uncover latent concepts within multilingual models . they introduce two metrics to quantify alignment and overlap of these concepts . |
| Outcome: | The proposed model can capture linguistic nuances across languages, but is not language-agnostic? the proposed model is able to capture nuances in multiple languages, the authors say. |
LAraBench: Benchmarking Arabic AI with Large Language Models (2024.eacl-long)
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Ahmed Abdelali, Hamdy Mubarak, Shammur Chowdhury, Maram Hasanain, Basel Mousi, Sabri Boughorbel, Samir Abdaljalil, Yassine El Kheir, Daniel Izham, Fahim Dalvi, Majd Hawasly, Nizi Nazar, Youssef Elshahawy, Ahmed Ali, Nadir Durrani, Natasa Milic-Frayling, Firoj Alam
| Challenge: | Recent advances in Large Language Models (LLMs) have significantly influenced the landscape of language and speech research. |
| Approach: | They used GPT-3.5-turbo, GPT-4, BLOOMZ, Jais-13b-chat, Whisper, and USM to tackle 33 distinct tasks across 61 datasets. |
| Outcome: | The proposed model outperforms SOTA models in zero-shot learning, with a few exceptions. |
OntologyRAG-Q: Resource Development and Benchmarking for Retrieval-Augmented Question Answering in Qur’anic Tafsir (2025.emnlp-main)
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| Challenge: | An annotated Tafsir ontology and a collection of 15 structured Tafsian books are presented in this paper. |
| Approach: | They propose a framework for retrieval and question-answering Tafsir data that spans the entire pipeline from dataset construction through evaluation and error analysis. |
| Outcome: | The proposed framework achieves 69.52% accuracy and 74.36% correctness overall, though multi-hop and context-dependent questions remain challenging. |
A Multi-Platform Arabic News Comment Dataset for Offensive Language Detection (2020.lrec-1)
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Shammur Absar Chowdhury, Hamdy Mubarak, Ahmed Abdelali, Soon-gyo Jung, Bernard J. Jansen, Joni Salminen
| Challenge: | Social media platforms allow users to engage in conversation with limited accountability, causing hate crimes and mental harm to targeted individuals. |
| Approach: | They propose to make public a new dialectal Arabic news comment dataset . they analyze distinctive lexical content along with the use of emojis in offensive comments . |
| Outcome: | The proposed dataset analyzes offensive language and distinctive lexical content along with the use of emojis on Twitter, Facebook, and YouTube. |
Arabic Curriculum Analysis (2020.coling-demos)
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| Challenge: | Effective language curricula are critical to teaching communication skills . a platform that analyzes curricular content can help identify shortcomings . |
| Approach: | They propose a platform that analyzes Arabic curricula and provides insights into their content. |
| Outcome: | The proposed system analyzes Arabic curricula and provides insights into their content . it provides statistics about word usage and morphological forms in different grades . |
Multi-Dialect Arabic POS Tagging: A CRF Approach (L18-1)
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Kareem Darwish, Hamdy Mubarak, Ahmed Abdelali, Mohamed Eldesouki, Younes Samih, Randah Alharbi, Mohammed Attia, Walid Magdy, Laura Kallmeyer
| Challenge: | Existing work on dialectal POS tagging is rather scant with POS tags for most dialects being nonexistent or of limited availability. |
| Approach: | They propose a dataset of POS-tagged Arabic tweets in four major dialects and a tagging guideline for each dialect. |
| Outcome: | The proposed model can tag four different dialects with an average accuracy of 89.3%. |
A System for Diacritizing Four Varieties of Arabic (D19-3)
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| Challenge: | Short vowels, aka diacritics, are omitted when writing different varieties of Arabic . diacritization is essential for language learning and text-to-speech applications . |
| Approach: | They propose a system for recovering diacritics in Arabic without short vowels . they use a character-based sequence-to-sequence deep learning model . |
| Outcome: | The proposed system beats all previous SOTA systems for Arabic varieties . it uses a character-based sequence-to-sequence deep learning model . |
LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking (2024.eacl-demo)
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Fahim Dalvi, Maram Hasanain, Sabri Boughorbel, Basel Mousi, Samir Abdaljalil, Nizi Nazar, Ahmed Abdelali, Shammur Absar Chowdhury, Hamdy Mubarak, Ahmed Ali
| Challenge: | Recent development and success of Large Language Models necessitate evaluation of their performance across diverse NLP tasks in different languages. |
| Approach: | They propose a framework that can be customized to evaluate LLMs for any NLP task, regardless of language. |
| Outcome: | The LLMeBench framework can be customized to evaluate LLMs for any NLP task, regardless of language. |
AraBench: Benchmarking Dialectal Arabic-English Machine Translation (2020.coling-main)
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| Challenge: | Existing efforts to translate Arabic dialects to English are limited due to the lack of evaluation benchmarks. |
| Approach: | They propose an evaluation suite for Arabic to English machine translation using existing Arabic resources. |
| Outcome: | The evaluation suite for Arabic to English machine translation is based on existing evaluation benchmarks. |
Highly Effective Arabic Diacritization using Sequence to Sequence Modeling (N19-1)
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| Challenge: | Arabic text is written without short vowels (or diacritics) their presence is essential for properly verbalizing Arabic . |
| Approach: | They propose a character-level sequence-to-sequence deep learning model that recovers both types of diacritics without the use of explicit feature engineering. |
| Outcome: | The proposed model outperforms all previous state-of-the-art models on overlapping windows of words . it achieves a word error rate (WER) of 4.49% compared to the state- of-the art systems . |
AraEval: An Arabic Multi-Task Evaluation Suite for Large Language Models (2025.emnlp-main)
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Alhanoof Althnian, Norah A. Alzahrani, Shaykhah Z. Alsubaie, Eman Albilali, Ahmed Abdelali, Nouf M. Alotaibi, M Saiful Bari, Yazeed Alnumay, Abdulhamed Alothaimen, Maryam Saif, Shahad D. Alzaidi, Faisal Abdulrahman Mirza, Yousef Almushayqih, Mohammed Al Saleem, Ghadah Alabduljabbar, Abdulmohsen Al-Thubaity, Areeb Alowisheq, Nora Al-Twairesh
| Challenge: | AraEval is a suite of evaluation tasks designed to assess the advanced knowledge, reasoning, truthfulness, and instruction following capabilities of large language models. |
| Approach: | They propose to use AraEval to assess the advanced knowledge, reasoning, truthfulness, and instruction following capabilities of large language models in the Arabic context. |
| Outcome: | The evaluation suite covers a broad spectrum of domains, including science, history, religion, and literature. |
Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society (2021.findings-emnlp)
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Firoj Alam, Shaden Shaar, Fahim Dalvi, Hassan Sajjad, Alex Nikolov, Hamdy Mubarak, Giovanni Da San Martino, Ahmed Abdelali, Nadir Durrani, Kareem Darwish, Abdulaziz Al-Homaid, Wajdi Zaghouani, Tommaso Caselli, Gijs Danoe, Friso Stolk, Britt Bruntink, Preslav Nakov
| Challenge: | a dataset of 16K manually annotated tweets is used to analyze disinformation . the democratic nature of social media has raised questions about the quality and the factuality of the information that is shared on these platforms. |
| Approach: | They use a dataset of manually annotated tweets to analyze COVID-19 disinformation . they show that tweets contain fake cures, rumors, conspiracy theories and xenophobia . |
| Outcome: | The proposed dataset shows that it is useful in monolingual vs. multilingual settings. |