In current research on automatic essay scoring, related work tends to focus more on evaluating the overall quality or a single trait of prompt-specific essays. However, when scoring essays in an educational context, it is essential not only to consider the overall score but also to provide feedback on various aspects of the writing. This helps students clearly identify areas for improvement, enabling them to engage in targeted practice. Although many methods have been proposed to address the scoring issue, they still suffer from insufficient learning of trait representations and overlook the diversity and correlations between trait scores in the scoring process. To address this problem, we propose a novel multi-trait essay scoring method based on Trait-Aware Mix-of-Experts Representation Learning. Our method obtains trait-specific essay representations using a Mix-of-Experts scoring architecture. Furthermore, based on this scoring architecture, we propose a diversified trait-expert method to learn distinguishable expert weights. And to facilitate multi-trait scoring, we introduce two trait correlation learning strategies that achieve learning the correlations among traits. Experimental results demonstrate the effectiveness of our method, and compared to existing methods, it achieves a further improvement in computational efficiency.

Cross-prompt trait scoring task aims to learn generalizable scoring capabilities from source- prompt data, enabling automatic scoring across multiple dimensions on unseen essays. Existing research on cross-prompt trait essay scoring primarily focuses on improving model generalization by obtaining prompt-invariant representations. In this paper, we approach the research problem from a different perspective on invariance learning and propose a scoring-invariant learning objective. This objective encourages the model to focus on intrinsic information within the essay that reflects its quality during training, thereby learning generic scoring features. To further enhance the model’s ability to score across multiple dimensions, we introduce a trait feature extraction network based on routing gates into the scoring architecture and propose a trait consistency scoring objective to encourage the model to balance the diversity of trait-specific features with scoring consistency across traits when learning trait-specific essay features. Extensive experiments demonstrate the effectiveness of our approach, showing advantages in multi-trait scoring performance and achieving significant improvements with low-resource prompts.