Work

CEAES: Bidirectional Reinforcement Learning Optimization for Consistent and Explainable Essay Assessment

Year: 2025

Type: conference-paper

Abstract: Most current automated essay quality assessment systems treat score prediction and feedback generation as separate tasks, overlooking the fact that scores provide a quantitative evaluation of quality, while feedback offers a qualitative assessment.Both aspects reflect essay quality from different perspectives, and they are inherently consistent and can reinforce each other.In this paper, we propose a novel bidirectional reinforcement learning framework that effectively utilizes this consistency constraint to jointly optimize score prediction and feedback generation, ensuring mutual reinforcement and alignment between them.In this way, our model is hope to obtain a simultaneous accurate ratings and consistent text feedback.We conducted extensive experiments on publicly available datasets.The results demonstrate that our approach surpasses the current state-of-theart models, enhancing both scoring accuracy and feedback quality.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/2025.acl-long.1273

Authors: Xia Li, Wenjing Pan

Institution: Guangdong University of Foreign Studies

Language: English

Topic: Student Assessment and Feedback

Subfield: Education

Field: Social Sciences

Domain: Social Sciences

SDG: Quality education

Open Access status: hybrid

Funders: National Natural Science Foundation of China, Ministry of Education of the People's Republic of China