Tashkeel, or Arabic Text Diacritization (ATD), greatly enhances the comprehension of Arabic text by removing ambiguity and minimizing the risk of misinterpretations caused by its absence. It plays a crucial role in improving Arabic text processing, particularly in applications such as text-to-speech and machine translation. This paper introduces a new approach to training ATD models. First, we finetuned two transformers, encoder-only and encoder-decoder, that were initialized from a pretrained character-based BERT. Then, we applied the Noisy-Student approach to boost the performance of the best model. We evaluated our models alongside 11 commercial and open-source models using two manually labeled benchmark datasets: WikiNews and our CATT dataset. Our findings show that our top model surpasses all evaluated models by relative Diacritic Error Rates (DERs) of 30.83\% and 35.21\% on WikiNews and CATT, respectively, achieving state-of-the-art in ATD. In addition, we show that our model outperforms GPT-4-turbo on CATT dataset by a relative DER of 9.36\%. We open-source our CATT models and benchmark dataset for the research community\footnote{https://github.com/abjadai/catt}.

通过训练Tashkeel模型，本研究提出了一种新方法来改进阿拉伯文本处理，特别是在语音合成和机器翻译等应用中，通过消除歧义和最小化解释风险，提高对阿拉伯文本的理解能力。通过使用预训练的基于字符的BERT模型初始化，应用Noisy-Student方法提升了模型的性能，并通过实验证明其在ATD领域的最新成果。

CATT：基于字符的阿拉伯语塔什基尔转换器