<![CDATA[Arabic diacritization is the task of adding diacritical marks to Arabic text to aid correct articulation, resolve lexical ambiguity, and support downstream natural language processing tasks. This study proposes a hybrid deep learning architecture that augments a pre-trained transformer encoder (AraBERTv02) with a sequential prediction head built on either a Bidirectional Long Short-Term Memory (BiLSTM) or a Bidirectional Gated Recurrent Unit (BiGRU) network, replacing the conventional linear classification layer. This design introduces an explicit sequential inductive bias at the output level, enabling the model to capture local label dependencies that are not modeled by a standard linear head. The models were trained and evaluated on the Tashkeela Processed (TP) benchmark corpus of fully diacritized Modern Standard Arabic. The BiLSTM based model achieves a Diacritic Error Rate (DER) of 1.82% and a Word Error Rate (WER) of 2.58% while the BiGRU variant has a DER of 1.84% and a WER of 2.62% both outperforming the prior state-of-the-art on the same benchmark (WER: 3.34%). Error analysis reveals a systematic confusion of short vowels, especially in verb-initial contexts where fatha and damma alternate to mark active and passive voice, illustrating the inherent morphological complexity of Arabic diacritization. The proposed architecture is also shown to be suitable for real-world deployment with an inference time of 9.22 ms and 8.74 ms per sentence for BiLSTM and BiGRU models respectively. Thus, both variants are compatible with near real-time applications such as Arabic text-to-speech systems and educational tools.]]>
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