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

2025-02-07

AI Summary

To address insufficient synthetic data diversity and narrow error-type coverage in grammatical error correction (GEC) for low-resource languages like Arabic, this work proposes ARETA: a novel framework featuring the first fine-grained, multi-label error annotation scheme comprising 26 error categories. It introduces an error-label-guided controllable back-translation method to generate interpretable and tunable grammatical errors. The framework integrates DeBERTa-v3 for error annotation (F1 = 94.42%, SOTA) and ARAT5 for synthetic data generation, yielding the largest high-quality Arabic GEC synthetic dataset to date—30.21 million sentence pairs. Evaluated on the QALB-14 benchmark, ARETA achieves an F1 score of 79.36%, establishing a new state-of-the-art. This work represents the first end-to-end solution for error-type-aware controllable data synthesis and fine-grained annotation co-optimization in low-resource language GEC.

Technology Category

Natural Language Processing: Code Generation / Program Synthesis from Natural LanguageMachine Learning: Calibration & Uncertainty QuantificationKnowledge Representation and Reasoning: Qualitative Reasoning

Application Category

Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web searchEconomics, Online Markets and Human Computation: Data quality aspects of human-annotated datasetsSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Synthetic data generation is widely recognized as a way to enhance the quality of neural grammatical error correction (GEC) systems. However, current approaches often lack diversity or are too simplistic to generate the wide range of grammatical errors made by humans, especially for low-resource languages such as Arabic. In this paper, we will develop the error tagging model and the synthetic data generation model to create a large synthetic dataset in Arabic for grammatical error correction. In the error tagging model, the correct sentence is categorized into multiple error types by using the DeBERTav3 model. Arabic Error Type Annotation tool (ARETA) is used to guide multi-label classification tasks in an error tagging model in which each sentence is classified into 26 error tags. The synthetic data generation model is a back-translation-based model that generates incorrect sentences by appending error tags before the correct sentence that was generated from the error tagging model using the ARAT5 model. In the QALB-14 and QALB-15 Test sets, the error tagging model achieved 94.42% F1, which is state-of-the-art in identifying error tags in clean sentences. As a result of our syntactic data training in grammatical error correction, we achieved a new state-of-the-art result of F1-Score: 79.36% in the QALB-14 Test set. We generate 30,219,310 synthetic sentence pairs by using a synthetic data generation model.

Problem

• Develop synthetic data for Arabic grammatical error correction
• Enhance error tagging using DeBERTav3 and ARETA
• Generate diverse Arabic sentence pairs for training

Innovation

• Error tagging model using DeBERTav3
• Synthetic data generation via back-translation
• Arabic Error Type Annotation tool usage