This paper presents the KSAA-2026 Shared Task on Arabic Speech Dictation with Automatic Diacritization, addressing a persistent challenge in Arabic NLP. The task focuses on transforming speech transcripts into fully diacritized Arabic text by leveraging both the speech signal and its undiacritized transcript. Unlike conventional ASR tasks that focus on transcription, this task integrates acoustic and textual information to improve diacritization accuracy. The shared task consists of two subtasks: (1) Data Contribution, where participants recorded and reviewed speech data through the VoiceWall platform, resulting in 2,160 recordings, and (2) Diacritization, where 5 teams developed systems that generate fully diacritized text from speech and undiacritized transcripts. The dataset includes approximately 5 hours of Modern Standard Arabic (MSA) and multi-dialectal speech with fully diacritized references. Experimental results show that several participant systems outperform the provided baselines, and that incorporating speech information and fine-tuning improves performance compared to text-only approaches. KSAA-2026 shared task establishes a benchmark for multimodal Arabic diacritization and supports the development of robust systems for applications in education, accessibility, and speech-driven text generation.

This paper outlines the KSAA-CAD shared task, highlighting the Contemporary Arabic Language Dictionary within the scenario of developing a Reverse Dictionary (RD) system and enhancing Word Sense Disambiguation (WSD) capabilities. The first KSAA-RD (Al-Matham et al., 2023) highlighted significant gaps in the domain of RDs, which are designed to retrieve words by their meanings or definitions. This shared task comprises two tasks: RD and WSD. The RD task focuses on identifying word embeddings that most accurately match a given definition, termed a “gloss,” in Arabic. Conversely, the WSD task involves determining the specific meaning of a word in context, particularly when the word has multiple meanings. The winning team achieved the highest-ranking score of 0.0644 in RD using Electra embeddings. In this paper, we describe the methods employed by the participating teams and provide insights into the future direction of KSAA-CAD.