@inproceedings{melanchthon-etal-2026-sentence,
    title = "Through the Sentence Lens: Explainable Essay Scoring through Fine-Grained Predictions",
    author = "Melanchthon, Daniel Mora  and
      Keller, Stefan  and
      Horbach, Andrea",
    editor = "Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bann{\`o}, Stefano  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Anais  and
      Yaneva, Victoria  and
      Yuan, Zheng",
    booktitle = "Proceedings of the 21st Workshop on Innovative Use of {NLP} for Building Educational Applications ({BEA} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bea-1.16/",
    pages = "221--233",
    ISBN = "979-8-89176-409-5",
    abstract = "Beyond performance, model transparency is a crucial factor in Automated Essay Scoring, yet current systems often lack explainability, limiting their pedagogical value and users' trust. Existing explainability methods, such as gradient-based attribution or feature-importance approaches, either produce counterintuitive explanations or are too complex for classroom use. To address this limitation, we make use of fine-grained prediction at the sentence level as a way to enhance explainability. We propose ablation strategies to derive sentence-level pseudo scores from essay-level gold scores and use them to train sentence-level models. We evaluate their performance against essay-level baselines on two datasets (ASAP and MEWS), and compare their sentence-level output to a human baseline. Results indicate a trade-off between essay-level performance and sentence-level granularity. For the language quality trait, most sentence-level models achieve performance comparable to the essay-level baseline, whereas for content, the approach yields more positive results on prompts with shorter"
}