@inproceedings{jeon-strube-2021-countering,
    title = "Countering the Influence of Essay Length in Neural Essay Scoring",
    author = "Jeon, Sungho  and
      Strube, Michael",
    editor = "Moosavi, Nafise Sadat  and
      Gurevych, Iryna  and
      Fan, Angela  and
      Wolf, Thomas  and
      Hou, Yufang  and
      Marasovi{\'c}, Ana  and
      Ravi, Sujith",
    booktitle = "Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Virtual",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.sustainlp-1.4/",
    doi = "10.18653/v1/2021.sustainlp-1.4",
    pages = "32--38",
    abstract = "Previous work has shown that automated essay scoring systems, in particular machine learning-based systems, are not capable of assessing the quality of essays, but are relying on essay length, a factor irrelevant to writing proficiency. In this work, we first show that state-of-the-art systems, recent neural essay scoring systems, might be also influenced by the correlation between essay length and scores in a standard dataset. In our evaluation, a very simple neural model shows the state-of-the-art performance on the standard dataset. To consider essay content without taking essay length into account, we introduce a simple neural model assessing the similarity of content between an input essay and essays assigned different scores. This neural model achieves performance comparable to the state of the art on a standard dataset as well as on a second dataset. Our findings suggest that neural essay scoring systems should consider the characteristics of datasets to focus on text quality."
}