@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/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T Representation-to-Creativity (R2C): Automated Holistic Scoring Model for Essay Creativity
%A Kim, Deokgi
%A Jo, Joonyoung
%A On, Byung-Won
%A Lee, Ingyu
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F kim-etal-2025-representation
%X 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.
%R 10.18653/v1/2025.findings-naacl.292
%U https://aclanthology.org/2025.findings-naacl.292/
%U https://doi.org/10.18653/v1/2025.findings-naacl.292
%P 5257-5275
Markdown (Informal)
[Representation-to-Creativity (R2C): Automated Holistic Scoring Model for Essay Creativity](https://aclanthology.org/2025.findings-naacl.292/) (Kim et al., Findings 2025)
ACL