We describe Abjad AI’s submission to KSAA-2026 Shared Task 2 on automatic diacritization of Arabic speech dictation. The task requires generating fully diacritized text given speech audio and an undiacritized transcript. Because text-only diacritization cannot resolve ambiguities that are recoverable from the acoustic signal, we propose conditioning a character-level encoder-only Transformer (CATT) (Alasmary et al., 2024) on speech representations. We introduce grouped speech conditioning, which downsamples speech encoder features into a small set of pooled tokens concatenated to the text input, enabling efficient fusion without architectural changes to CATT. We train with a two-phase schedule that first freezes the text encoder, then fine-tunes the full model. Our best system, using Whisper-small (Rad-ford et al., 2022) features with five grouped tokens, achieves a Diacritization Error Rate (DER) of 6.60 and a Word Error Rate (WER) of 18.66 (without case endings, including no-diacritic) on the official test set. Notably, we find that Whisper-small consistently outperforms Whisper-large-v3, suggesting that compact speech representations better suit this fusion setting. This is an extended and revised version of our previous work (Ghannam et al., 2025).

The translation between Modern Standard Arabic (MSA) and the various Arabic dialects presents unique challenges due to the significant linguistic, cultural, and contextual variations across the regions where Arabic is spoken. This paper presents a system description of our participation in the OSACT 2024 Dialect to MSA Translation Shared Task. We explain our comprehensive approach which combines data augmentation techniques using generative pre-trained transformer models (GPT-3.5 and GPT-4) with fine-tuning of AraT5 V2, a model specifically designed for Arabic translation tasks. Our methodology has significantly expanded the training dataset, thus improving the model’s performance across five major Arabic dialects, namely Gulf, Egyptian, Levantine, Iraqi, and Maghrebi. We have rigorously evaluated our approach, using BLEU score, to ensure translation accuracy, fluency, and the preservation of meaning. Our results showcase the effectiveness of our refined models in addressing the challenges posed by diverse Arabic dialects and Modern Standard Arabic (MSA), achieving a BLEU score of 80% on the validation test set and 22.25% on the blind test set. However, it’s important to note that while utilizing a larger dataset, such as Madar + Dev, resulted in significantly higher evaluation BLEU scores, the performance on the blind test set was relatively lower. This observation underscores the importance of dataset size in model training, revealing potential limitations in generalization to unseen data due to variations in data distribution and domain mismatches.

This study undertakes a comprehensive investigation of transformer-based models to advance Arabic language processing, focusing on two pivotal aspects: the estimation of Arabic Level of Dialectness and dialectal sentence-level machine translation into Modern Standard Arabic. We conducted various evaluations of different sentence transformers across a proposed regression model, showing that the MARBERT transformer-based proposed regression model achieved the best root mean square error of 0.1403 for Arabic Level of Dialectness estimation. In parallel, we developed bi-directional translation models between Modern Standard Arabic and four specific Arabic dialects—Egyptian, Emirati, Jordanian, and Palestinian—by fine-tuning and evaluating different sequence-to-sequence transformers. This approach significantly improved translation quality, achieving a BLEU score of 0.1713. We also enhanced our evaluation capabilities by integrating MSA predictions from the machine translation model into our Arabic Level of Dialectness estimation framework, forming a comprehensive pipeline that not only demonstrates the effectiveness of our methodologies but also establishes a new benchmark in the deployment of advanced Arabic NLP technologies.

Semantic search tasks have grown extremely fast following the advancements in large language models, including the Reverse Dictionary and Word Sense Disambiguation in Arabic. This paper describes our participation in the Contemporary Arabic Dictionary Shared Task. We propose two models that achieved first place in both tasks. We conducted comprehensive experiments on the latest five multilingual sentence transformers and the Arabic BERT model for semantic embedding extraction. We achieved a ranking score of 0.06 for the reverse dictionary task, which is double than last year’s winner. We had an accuracy score of 0.268 for the Word Sense Disambiguation task.

This paper describes our participation in the ArAIEval Shared Task 2024, focusing on Task 2C, which challenges participants to detect propagandistic elements in multimodal Arabic memes. The challenge involves analyzing both the textual and visual components of memes to identify underlying propagandistic messages. Our approach integrates the capabilities of MARBERT and ResNet50, top-performing pre-trained models for text and image processing, respectively. Our system architecture combines these models through a fusion layer that integrates and processes the extracted features, creating a comprehensive representation that is more effective in detecting nuanced propaganda. Our proposed system achieved significant success, placing second with an F1 score of 0.7987.