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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generation Model

2025-08-18

Abstract excerpt

<title>Abstract</title> <p>Synthetic data generation is widely recognized as an approach to improve the quality of neural grammatical error correction (GEC) systems. However, current approaches often lack diversity or are overly simplistic in generating the wide range of grammatical errors made by humans, particularly for low-resource languages such as Arabic. In this study, we developed an error tagging model an...

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Literature Corpus work: 8db87a25-9a53-5982-ac09-45bfdd4d3772
DOI: 10.21203/rs.3.rs-7049585/v1

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