@inproceedings{ahad-etal-2026-corpora,
    title = "Corpora Generation for {U}rdu Grammatical Error Correction",
    author = "Ahad, Syed  and
      Ezzi, Burhanuddin Aliasghar  and
      Hussain, Muhammad Arsalan  and
      Kumar, Sandesh  and
      Samad, Abdul",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.2156/",
    pages = "43428--43444",
    ISBN = "979-8-89176-395-1",
    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. We introduce UrduGEC-Synthetic, a synthetically generated dataset produced through this pipeline. Furthermore, we introduce UrduGEC-Gold, a Gold Dataset by extracting errors from exam copies of students. Finally, we also fine-tuned various models on UrduGEC-Synthetic and evaluated them against UrduGEC-Gold to show the quality of synthetic data generation."
}