@inproceedings{zhang-litman-2021-essay,
    title = "Essay Quality Signals as Weak Supervision for Source-based Essay Scoring",
    author = "Zhang, Haoran  and
      Litman, Diane",
    editor = "Burstein, Jill  and
      Horbach, Andrea  and
      Kochmar, Ekaterina  and
      Laarmann-Quante, Ronja  and
      Leacock, Claudia  and
      Madnani, Nitin  and
      Pil{\'a}n, Ildik{\'o}  and
      Yannakoudakis, Helen  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.bea-1.9/",
    pages = "85--96",
    abstract = "Human essay grading is a laborious task that can consume much time and effort. Automated Essay Scoring (AES) has thus been proposed as a fast and effective solution to the problem of grading student writing at scale. However, because AES typically uses supervised machine learning, a human-graded essay corpus is still required to train the AES model. Unfortunately, such a graded corpus often does not exist, so creating a corpus for machine learning can also be a laborious task. This paper presents an investigation of replacing the use of human-labeled essay grades when training an AES system with two automatically available but weaker signals of essay quality: word count and topic distribution similarity. Experiments using two source-based essay scoring (evidence score) corpora show that while weak supervision does not yield a competitive result when training a neural source-based AES model, it can be used to successfully extract Topical Components (TCs) from a source text, which are required by a supervised feature-based AES model. In particular, results show that feature-based AES performance is comparable with either automatically or manually constructed TCs."
}