Representation-to-Creativity (R2C): Automated Holistic Scoring Model for Essay Creativity
Deokgi Kim, Joonyoung Jo, Byung-Won On, Ingyu Lee
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.
BibTeX
@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://aclanthology.org/2025.findings-naacl.292/",
pages = "5257--5275",
ISBN = "979-8-89176-195-7"
}