Graph-Based Multi-Trait Essay Scoring

Shengjie Li and Vincent Ng.
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025.

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Abstract

While virtually all existing work on Automated Essay Scoring (AES) models an essay as a word sequence, we put forward the novel view that an essay can be modeled as a graph and subsequently propose GAT-AES, a graph-attention network approach to AES. A key advantage of a graph-based approach to AES is that it allows us to easily capture the interactions among essay traits directly. Experimental results show that GAT-AES has achieved the best multi-trait scoring results to date on the ASAP++ dataset.

Code

Our code and trained checkpoint are available here.

BibTeX entry

@InProceedings{Li+Ng:25a,
  author = {Shengjie Li and Vincent Ng},
  title = {Graph-Based Multi-Trait Essay Scoring},
  booktitle = {Proceedings of the 2025 Empirical Methods in Natural Language Processing},

  year = 2025}