Data Augmentation Techniques for Arabic Short Text Processing

Comparative Insights and Future Directions

Authors

  • Ali Muftah Benomran Department of Computer Science, Faculty information technology, Alasmarya Islamic University, Zliten, Libya
  • Ashrf Ali Nasef Department of Computer Science, Faculty of Science, Alasmarya Islamic University, Zliten Libya
  • Aimen M.Rmis Department of Computer Science, Faculty of Science, Alasmarya Islamic University, Zliten, Libya
  • Salem Husein Almadhun Department of Computer, Faculty of Education, Elmergib University, Al Khums, Libya
  • Mamamer M Awinat Department of Computer Science, Faculty information technology, Alasmarya Islamic University, Zliten, Libya

DOI:

https://doi.org/10.59743/jaf.v10i1.960

Keywords:

Arabic NLP, Data Augmentation, Short Text Processing, Dialectal Variation, Low-Resource Languages, Comparative Review

Abstract

Arabic short texts pose challenges due to morphological complexity, dialectal diversity, and data scarcity. Data augmentation offers a solution, yet no review systematically compares techniques across tasks. We examine six strategies across five tasks using 22 studies (2019–2026). Results show no universal method works: synonym replacement boosts topic classification but harms NER; back-translation fails under dialectal shift; generative models improve sentiment analysis when filtered; feature-space resampling inflates metrics artificially. Three persistent issues cut across tasks: the diversity–fidelity trade-off, dialectal contamination, and computational cost. We propose a focused agenda emphasizing contrastive sampling, bias-aware generation, multimodal integration, and lightweight augmentation for low-resource dialects. This review provides actionable guidance for task-aware Arabic NLP frameworks.

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Published

30-06-2026

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Vol. 10 No. 1 (2026)

Section

Applied Sciences

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Copyright (c) 2026 علي مفتاح بن عمران ، أشرف علي ناصف، أيمن مختار ارميص، سالم حسين المدهون، معمر مصباح عوينات

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How to Cite

Benomran, A. M. ., Nasef, A. A. ., Rmis, A. M., Almadhun, S. H. ., & Awinat, M. M. . (2026). Data Augmentation Techniques for Arabic Short Text Processing: Comparative Insights and Future Directions. Journal of the Academic Forum, 10(1), 153-179. https://doi.org/10.59743/jaf.v10i1.960