@inproceedings{li-ng-2026-cross,
    title = "Cross-Prompt Automated Essay Scoring of Multiple Traits: Making Sense of the State of the Art",
    author = "Li, Shengjie  and
      Ng, Vincent",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.2105/",
    pages = "45384--45406",
    ISBN = "979-8-89176-390-6",
    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."
}