@inproceedings{song-etal-2025-unified,
    title = "Unified Automated Essay Scoring and Grammatical Error Correction",
    author = "Song, SeungWoo  and
      Yuk, Junghun  and
      Choi, ChangSu  and
      Yoo, HanGyeol  and
      Lim, HyeonSeok  and
      Lim, KyungTae  and
      Park, Jungyeul",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.250/",
    pages = "4412--4426",
    ISBN = "979-8-89176-195-7",
    abstract = "This study explores the integration of automated writing evaluation (AWE) and grammatical error correction (GEC) through multitask learning, demonstrating how combining these distinct tasks can enhance performance in both areas. By leveraging a shared learning framework, we show that models trained jointly on AWE and GEC outperform those trained on each task individually. To support this effort, we introduce a dataset specifically designed for multitask learning using AWE and GEC. Our experiments reveal significant synergies between tasks, leading to improvements in both writing assessment accuracy and error correction precision. This research represents a novel approach for optimizing language learning tools by unifying writing evaluation and correction tasks, offering insights into the potential of multitask learning in educational applications."
}