Corpora Generation for Urdu Grammatical Error Correction

ACL ARR 2026 January Submission10879 Authors

Keywords: NLP, GEC, Corpora Generation

Abstract: Grammatical Error Correction (GEC) for Urdu remains an under-researched area due to the lack of annotated datasets. This paper addresses the challenge of generating a robust corpus for fine-tuning deep learning models aimed at Urdu GEC. We propose a method for synthesizing a large dataset by collecting errors from the Urdu WikiEdits history, learning from them, and inserting similar errors in grammatically correct sentences to generate incorrect sentences with grammatical errors, hence creating a pair of grammatically correct and incorrect sentences. Furthermore, we have created a Gold Dataset by extracting errors from exam copies of students. After the generation of the dataset, we have also fine-tuned various models against synthetically generated data and evaluated them against gold data to show the quality of synthetic data generation.

Paper Type: Long

Research Area: Low-resource Methods for NLP

Research Area Keywords: GEC, Urdu, NLP

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Approaches to low-resource settings, Data analysis

Languages Studied: Urdu

Submission Number: 10879