A Multi-Level Arabic Text Diacritization System

Ali Mijlad () and Yacine El Younoussi ()
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Ali Mijlad: ENSATe, Abdelmalek Essaadi University, SIGL Laboratory
Yacine El Younoussi: ENSATe, Abdelmalek Essaadi University, SIGL Laboratory

A chapter in Technological Innovations for Sustainable Development, 2025, pp 17-27 from Springer

Abstract: Abstract This paper presents a multi-level Arabic diacritization system designed to restore diacritics for undiacritized Arabic text. This kind of systems is crucial for Arabic-related NLP tasks and aids learners and individuals with learning difficulties, such as dyslexia or visual impairments. Our system uses a two-level approach: a word-based level and a letter-based level, both employing an encoder-decoder model with a local predictive Luong attention mechanism. The combined model demonstrates good performance with a 22.47% diacritic error rate, significantly surpassing single-level models while maintaining competitive performance despite using a smaller dataset compared to previous studies.

Keywords: Diacritization; Arabic; GRU; Encoder-Decoder Model; Luong Attention (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-032-06725-8_2

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DOI: 10.1007/978-3-032-06725-8_2

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