@inproceedings{bexte-etal-2026-aslan,
    title = "{ASLAN} at {BEA} 2026 Shared Task 2: Voting Across Scoring Paradigms",
    author = "Bexte, Marie  and
      Ding, Yuning  and
      Ruppenhofer, Josef  and
      Schaller, Nils-Jonathan  and
      Melanchthon, Daniel Mora  and
      Zesch, Torsten  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-1.87/",
    pages = "1201--1209",
    ISBN = "979-8-89176-409-5",
    abstract = "This paper describes the ASLAN system contribution to the BEA 2026 Shared Task on rubric-based short answer scoring for German (Gombert et al., 2026). We investigate three complementary modeling paradigms: similarity-based scoring, instance-based classification, and rubric-prompted large language models (LLMs). For the unseen answers track, where test answers belong to prompts observed during training, we compare question-specific and generic scoring models as well as ensemble variants. For the unseen questions track, where models must generalize to previously unseen prompts, we primarily rely on zero-shot LLM-based scoring using the scoring rubrics. Our experiments show that similarity-based models outperform instance-based models and LLM-based models in the unseen answers setting. In addition, we find that ensemble methods improve robustness over individual models."
}