CATT: Character-based Arabic Tashkeel Transformer

• URL: http://arxiv.org/abs/2407.03236v3
• Date: Sun, 14 Jul 2024 10:01:40 GMT
• Title: CATT: Character-based Arabic Tashkeel Transformer
• Authors: Faris Alasmary, Orjuwan Zaafarani, Ahmad Ghannam,
• Abstract summary: Tashkeel, or Arabic Text Diacritization, greatly enhances the comprehension of Arabic text. This paper introduces a new approach to training ATD models. We evaluate our models alongside 11 commercial and open-source models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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}.

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