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Tibyan corpus: balanced and comprehensive error coverage corpus using ChatGPT for Arabic grammatical error

Ahlam Alrehili1,2, Areej Alhothali1

  • 1Department of Computer Sciences, Faculty of Computing and Information Technology, King Abdul Aziz University, Jeddah, Saudi Arabia.

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Summary

This study introduces Tibyan, a new Arabic corpus for grammatical error correction (GEC). It uses ChatGPT to augment data, addressing the scarcity of resources for Arabic GEC.

Keywords:
AraGECArabic grammatical error correctionChatGPTCorpusGECNLP

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Area of Science:

  • Computational Linguistics
  • Natural Language Processing
  • Corpus Linguistics

Background:

  • Scarce and low-quality data pose challenges in Natural Language Processing (NLP).
  • Arabic has limited resources for Grammatical Error Correction (GEC) despite its widespread use.
  • Data augmentation is crucial for improving NLP model performance, especially in low-resource languages.

Purpose of the Study:

  • To develop a novel Arabic corpus, "Tibyan," specifically for Grammatical Error Correction (GEC).
  • To leverage ChatGPT as a data augmentation tool for creating Arabic GEC data.
  • To address the limitations of existing Arabic NLP resources.

Main Methods:

  • Collected and pre-processed Arabic texts from diverse sources.
  • Utilized ChatGPT to generate a parallel corpus of grammatically incorrect and correct Arabic sentences.
  • Engaged linguistic experts for iterative validation and refinement of the generated corpus.
  • Analyzed error types using the Arabic Error Type Annotation (ARETA) tool.

Main Results:

  • The Tibyan corpus contains approximately 600,000 tokens.
  • The corpus includes 49% of errors across seven categories: orthography, morphology, syntax, semantics, punctuation, merge, and split.
  • Expert validation ensured the accuracy and quality of the generated data.

Conclusions:

  • The Tibyan corpus provides a valuable resource for advancing Arabic Grammatical Error Correction (GEC).
  • The methodology demonstrates an effective approach to data augmentation for low-resource languages using large language models.
  • This work contributes to improving NLP applications for the Arabic language.