Bidirectional Encoder Representations from Transformers (BERT) has gained increasing
attention from researchers and practitioners as it has proven to be an invaluable technique …

We introduce Jais and Jais-chat, new state-of-the-art Arabic-centric foundation and
instruction-tuned open generative large language models (LLMs). The models are based on …

Transfer learning with a unified Transformer framework (T5) that converts all language
problems into a text-to-text format was recently proposed as a simple and effective transfer …

Topic modeling is an unsupervised machine learning technique for finding abstract topics in
a large collection of documents. It helps in organizing, understanding and summarizing …

We present an overview of the ArAIEval shared task, organized as part of the first ArabicNLP
2023 conference co-located with EMNLP 2023. ArAIEval offers two tasks over Arabic text:(i) …

We introduce a generic, language-independent method to collect a large percentage of
offensive and hate tweets regardless of their topics or genres. We harness the extralinguistic …

The rapid progress in deep learning has propelled transformer-based models to the
forefront, establishing them as leading solutions for a multiple NLP tasks. These tasks span …

We present WojoodNER-2023, the first Arabic Named Entity Recognition (NER) Shared
Task. The primary focus of WojoodNER-2023 is on Arabic NER, offering novel NER datasets …

Pre-trained transformers are now the de facto models in Natural Language Processing given
their state-of-the-art results in many tasks and languages. However, most of the current …

Accurate assignment of meaning to a word based on its context, known as Word Sense
Disambiguation (WSD), remains challenging across languages. Extensive research aims to …