@inproceedings{kim-etal-2025-representation,
    title = "Representation-to-Creativity ({R}2{C}): Automated Holistic Scoring Model for Essay Creativity",
    author = "Kim, Deokgi  and
      Jo, Joonyoung  and
      On, Byung-Won  and
      Lee, Ingyu",
    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.292/",
    pages = "5257--5275",
    ISBN = "979-8-89176-195-7",
    abstract = "Despite active research on Automated Essay Scoring (AES), there is a noticeable scarcity of studies focusing on predicting creativity scores for essays. In this study, we develop a new essay rubric specifically designed for assessing creativity in essays. Leveraging this rubric, we construct ground truth data consisting of 5,048 essays. Furthermore, we propose a novel self-supervised learning model that recognizes cluster patterns within the essay embedding space and leverages them for creativity scoring. This approach aims to automatically generate a high-quality training set, thereby facilitating the training of diverse language models. Our experimental findings indicated a substantial enhancement in the assessment of essay creativity, demonstrating an increase in F1-score up to 58{\%} compared to the primary state-of-the-art models across the ASAP and AIHUB datasets."
}