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        <title>Cross-Prompt Automated Essay Scoring of Multiple Traits: Making Sense of the State of the Art</title>
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    <abstract>Despite the recent progress made in cross-prompt essay scoring, there is little analysis of what makes a state-of-the-art cross-prompt scorer work well. To this end, we present an empirical analysis of how the key components of a cross-prompt scorer interact with each other and impact its overall performance. In addition, we examine for the first time the application of transductive learning to cross-prompt scoring, which represents an important starting point for providing a practical way to improve cross-prompt scorers for use in the rarely-studied classroom setting without the need for additional labeled training data.</abstract>
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%0 Conference Proceedings
%T Cross-Prompt Automated Essay Scoring of Multiple Traits: Making Sense of the State of the Art
%A Li, Shengjie
%A Ng, Vincent
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F li-ng-2026-cross
%X Despite the recent progress made in cross-prompt essay scoring, there is little analysis of what makes a state-of-the-art cross-prompt scorer work well. To this end, we present an empirical analysis of how the key components of a cross-prompt scorer interact with each other and impact its overall performance. In addition, we examine for the first time the application of transductive learning to cross-prompt scoring, which represents an important starting point for providing a practical way to improve cross-prompt scorers for use in the rarely-studied classroom setting without the need for additional labeled training data.
%U https://aclanthology.org/2026.acl-long.2105/
%P 45384-45406

Markdown (Informal)

[Cross-Prompt Automated Essay Scoring of Multiple Traits: Making Sense of the State of the Art](https://aclanthology.org/2026.acl-long.2105/) (Li & Ng, ACL 2026)

• Cross-Prompt Automated Essay Scoring of Multiple Traits: Making Sense of the State of the Art (Li & Ng, ACL 2026)

ACL

• Shengjie Li and Vincent Ng. 2026. Cross-Prompt Automated Essay Scoring of Multiple Traits: Making Sense of the State of the Art. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 45384–45406, San Diego, California, United States. Association for Computational Linguistics.