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We present a comprehensive comparative analysis of different transformer models for the task of punctuation prediction in Arabic text. The models evaluated include Asafaya-BERT, XLM-RoBERTa, Google BERT Multi-lingual, AraBERT, MarBERT and AUBMindLab’s AraGPT-2. Each model’s performance was assessed using accuracy, precision and F1 score metrics across multiple punctuation marks, providing a detailed understanding of their strengths and limitations. The results highlight AraBERT and MarBERT as particularly effective for predicting Arabic punctuation, with detailed metrics demonstrating their superior performance in both general and specific punctuation scenarios. This analysis provides valuable insights for future research and practical applications in Arabic natural language processing.

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A Comparative Analysis of Transformer Models for the Prediction of Arabic Punctuation

  • 会议论文

  • 发表日期：

    2025年

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  • Abdelkarim Aboutaib,
  • Ahmad El Allaoui,
  • Imad Zeroual

摘要

We present a comprehensive comparative analysis of different transformer models for the task of punctuation prediction in Arabic text. The models evaluated include Asafaya-BERT, XLM-RoBERTa, Google BERT Multi-lingual, AraBERT, MarBERT and AUBMindLab’s AraGPT-2. Each model’s performance was assessed using accuracy, precision and F1 score metrics across multiple punctuation marks, providing a detailed understanding of their strengths and limitations. The results highlight AraBERT and MarBERT as particularly effective for predicting Arabic punctuation, with detailed metrics demonstrating their superior performance in both general and specific punctuation scenarios. This analysis provides valuable insights for future research and practical applications in Arabic natural language processing.