An interpretable automated essay scoring model for English compositions based on SHAP algorithm

• Published in: 智能科学与技术学报 Vol. 7; pp. 370 - 380
• Main Authors: WANG Bing, SHAN Ruixue, XING Haiyan, LI Panchi
• Format: Journal Article
• Language: Chinese
• Published: POSTS&TELECOM PRESS Co., LTD 01.09.2025
• Subjects: automated essay scoring; E5-SHAP algorithm; English composition; interpretability
• ISSN: 2096-6652

In response to the lack of interpretability in English composition automatic scoring systems due to their reliance on complex deep learning models, an interpretable English composition automatic scoring model was proposed based on the E5-SHAP algorithm. This model was based on the E5 base model encoder to extract text features, combined with a mean calculation and a regression layer to achieve scoring output. It introduced an adaptive weighting mechanism to comprehensively evaluate the quality of compositions across six dimensions, including grammar, syntax, and vocabulary diversity. The model utilized LoRA fine-tuning technology to optimize specific layer parameters and enhance adaptability to compositional features. By using the SHAP algorithm to calculate the impact of each feature on the final score, a clear scoring basis and explanation path was provided to enhance the transparency and credibility of the scoring process. The experimental results show that compared with existing models, the performance of