@inproceedings{belcher-etal-2026-iwm,
    title = "{IWM}-{DKM} at {BEA} 2026 Shared Task 2: Supplementing Supervised Fine-Tuning for Rubric-Based Short Answer Scoring",
    author = "Belcher, Kate  and
      De Kuthy Meurers, Marius  and
      De Kuthy, Kordula  and
      Meurers, Detmar",
    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-1.90/",
    pages = "1224--1234",
    ISBN = "979-8-89176-409-5",
    abstract = "In this paper, we present the IWM-DKM team submissions to the BEA 2026 Shared Task 2: Rubric-based Short Answer Scoring for German. We systematically explored how fine-tuned language models can be reliably employed for short answer scoring, for which three aspects turn out to be particularly beneficial: supplementing the fine-tuning process with generated domain expertise, restructured rubrics, and thinking traces. To increase the robustness of the scoring, we combine distinct approaches in an ensemble. Our best submissions finished in first place across all tracks, indicating promise for the further application of these strategies in automatic scoring."
}