Cross-Prompt Automated Essay Scoring via Reinforcement Learning-Based Data Valuation

Automated essay scoring (AES) aims to automatically grade essays, thereby reducing the time and cost associated with manual scoring. The most common AES methods are classified under the prompt-specific approach, which involves developing a scoring model exclusively for a target prompt by using a dataset of scored essays corresponding to that prompt. Meanwhile, recent studies have emphasized the cross-prompt approach. This method leverages scored essay data from other prompts, referred to as source prompts, to build an AES model for the target prompt. However, these cross-prompt methods have limitations in that they do not consider the presence of source-prompt essays that can potentially have a negative impact on the construction of the AES model for the target prompt. To address this limitation, we propose a novel cross-prompt AES method that utilizes data valuation based on reinforcement learning (DVRL). The proposed method enables the value-driven use of source-prompt essays, which positively contributes to improving the scoring accuracy of the AES for the target prompt. Experiments on a benchmark dataset demonstrate that the proposed method enhances the performance of various AES models in cross-prompt scoring settings.