Arabic Text Diacritization In The Age Of Transfer Learning: Token Classification Is All You Need
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917563460485120 |
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| author | Skiredj, Abderrahman Berrada, Ismail |
| author_facet | Skiredj, Abderrahman Berrada, Ismail |
| contents | Automatic diacritization of Arabic text involves adding diacritical marks (diacritics) to the text. This task poses a significant challenge with noteworthy implications for computational processing and comprehension. In this paper, we introduce PTCAD (Pre-FineTuned Token Classification for Arabic Diacritization, a novel two-phase approach for the Arabic Text Diacritization task. PTCAD comprises a pre-finetuning phase and a finetuning phase, treating Arabic Text Diacritization as a token classification task for pre-trained models. The effectiveness of PTCAD is demonstrated through evaluations on two benchmark datasets derived from the Tashkeela dataset, where it achieves state-of-the-art results, including a 20\% reduction in Word Error Rate (WER) compared to existing benchmarks and superior performance over GPT-4 in ATD tasks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_04848 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Arabic Text Diacritization In The Age Of Transfer Learning: Token Classification Is All You Need Skiredj, Abderrahman Berrada, Ismail Computation and Language Automatic diacritization of Arabic text involves adding diacritical marks (diacritics) to the text. This task poses a significant challenge with noteworthy implications for computational processing and comprehension. In this paper, we introduce PTCAD (Pre-FineTuned Token Classification for Arabic Diacritization, a novel two-phase approach for the Arabic Text Diacritization task. PTCAD comprises a pre-finetuning phase and a finetuning phase, treating Arabic Text Diacritization as a token classification task for pre-trained models. The effectiveness of PTCAD is demonstrated through evaluations on two benchmark datasets derived from the Tashkeela dataset, where it achieves state-of-the-art results, including a 20\% reduction in Word Error Rate (WER) compared to existing benchmarks and superior performance over GPT-4 in ATD tasks. |
| title | Arabic Text Diacritization In The Age Of Transfer Learning: Token Classification Is All You Need |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2401.04848 |