Arabic Natural Language Processing
Arabic Natural Language Processing (Arabic NLP) aims to enable computers to understand and process Arabic text and speech, addressing the challenges posed by its rich morphology and diverse dialects. Current research heavily focuses on developing and improving large language models (LLMs), often based on transformer architectures, and applying them to tasks like question answering, machine translation, sentiment analysis, and dialect identification. These advancements are crucial for bridging the language gap in various applications, including information retrieval, chatbot development, and social media analysis, ultimately fostering broader access to information and technology in Arabic-speaking communities.
Papers
LAraBench: Benchmarking Arabic AI with Large Language Models
Ahmed Abdelali, Hamdy Mubarak, Shammur Absar Chowdhury, Maram Hasanain, Basel Mousi, Sabri Boughorbel, Yassine El Kheir, Daniel Izham, Fahim Dalvi, Majd Hawasly, Nizi Nazar, Yousseif Elshahawy, Ahmed Ali, Nadir Durrani, Natasa Milic-Frayling, Firoj Alam
GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP
Md Tawkat Islam Khondaker, Abdul Waheed, El Moatez Billah Nagoudi, Muhammad Abdul-Mageed
The Shared Task on Gender Rewriting
Bashar Alhafni, Nizar Habash, Houda Bouamor, Ossama Obeid, Sultan Alrowili, Daliyah Alzeer, Khawlah M. Alshanqiti, Ahmed ElBakry, Muhammad ElNokrashy, Mohamed Gabr, Abderrahmane Issam, Abdelrahim Qaddoumi, K. Vijay-Shanker, Mahmoud Zyate
A Benchmark Study of Contrastive Learning for Arabic Social Meaning
Md Tawkat Islam Khondaker, El Moatez Billah Nagoudi, AbdelRahim Elmadany, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan