@inproceedings{afrin-litman-2023-predicting,
    title = "Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing",
    author = "Afrin, Tazin  and
      Litman, Diane",
    editor = "Vlachos, Andreas  and
      Augenstein, Isabelle",
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2023.findings-eacl.193/",
    doi = "10.18653/v1/2023.findings-eacl.193",
    pages = "2550--2561",
    abstract = "We develop models to classify desirable evidence and desirable reasoning revisions in student argumentative writing. We explore two ways to improve classifier performance {--} using the essay context of the revision, and using the feedback students received before the revision. We perform both intrinsic and extrinsic evaluation for each of our models and report a qualitative analysis. Our results show that while a model using feedback information improves over a baseline model, models utilizing context - either alone or with feedback - are the most successful in identifying desirable revisions."
}