@inproceedings{chen-li-2024-plaes,
    title = "{PLAES}: Prompt-generalized and Level-aware Learning Framework for Cross-prompt Automated Essay Scoring",
    author = "Chen, Yuan  and
      Li, Xia",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1118",
    pages = "12775--12786",
    abstract = "Current cross-prompt automatic essay scoring (AES) systems are primarily concerned with obtaining shared knowledge specific to the target prompt by using the source and target prompt essays. However, it may not be feasible in practical situations because the target prompt essays may not be available as training data. When constructing a model solely from source prompt essays, its capacity to generalize to the target prompt may be hindered by significant discrepancies among different prompt essays. In this study, a novel learning framework for cross-prompt AES is proposed in order to capture more general knowledge across prompts and improve the model{'}s capacity to distinguish between writing levels. To acquire generic knowledge across different prompts, a primary model is trained via meta learning with all source prompt essays. To improve the model{'}s ability to differentiate writing levels, we present a level-aware learning strategy consisting of a general scorer and three level scorers for low-, middle-, and high-level essays. Then, we introduce a contrastive learning strategy to bring the essay representation of the general scorer closer to its corresponding level representation and far away from the other two levels, thereby improving the system{'}s ability to differentiate writing levels as well as boosting scoring performance. Experimental results on public datasets illustrate the efficacy of our method.",
}

Current cross-prompt automatic essay scoring (AES) systems are primarily concerned with obtaining shared knowledge specific to the target prompt by using the source and target prompt essays. However, it may not be feasible in practical situations because the target prompt essays may not be available as training data. When constructing a model solely from source prompt essays, its capacity to generalize to the target prompt may be hindered by significant discrepancies among different prompt essays. In this study, a novel learning framework for cross-prompt AES is proposed in order to capture more general knowledge across prompts and improve the model{'}s capacity to distinguish between writing levels. To acquire generic knowledge across different prompts, a primary model is trained via meta learning with all source prompt essays. To improve the model{'}s ability to differentiate writing levels, we present a level-aware learning strategy consisting of a general scorer and three level scorers for low-, middle-, and high-level essays. Then, we introduce a contrastive learning strategy to bring the essay representation of the general scorer closer to its corresponding level representation and far away from the other two levels, thereby improving the system{'}s ability to differentiate writing levels as well as boosting scoring performance. Experimental results on public datasets illustrate the efficacy of our method.
Chen, Yuan Li, Xia
PLAES: Prompt-generalized and Level-aware Learning Framework for Cross-prompt Automated Essay Scoring