@inproceedings{wang-etal-2018-automatic,
    title = "Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning",
    author = "Wang, Yucheng  and
      Wei, Zhongyu  and
      Zhou, Yaqian  and
      Huang, Xuanjing",
    editor = "Riloff, Ellen  and
      Chiang, David  and
      Hockenmaier, Julia  and
      Tsujii, Jun{'}ichi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/D18-1090/",
    doi = "10.18653/v1/D18-1090",
    pages = "791--797",
    abstract = "Automatic essay scoring (AES) is the task of assigning grades to essays without human interference. Existing systems for AES are typically trained to predict the score of each single essay at a time without considering the rating schema. In order to address this issue, we propose a reinforcement learning framework for essay scoring that incorporates quadratic weighted kappa as guidance to optimize the scoring system. Experiment results on benchmark datasets show the effectiveness of our framework."
}