@inproceedings{gombert-etal-2026-rubrics,
    title = "Rubrics as Semantic Subspaces: A Unified Approach to Rubric-based Constructed Response Scoring across Short Answers and Essays",
    author = "Gombert, Sebastian  and
      Hahn, Sonja  and
      Andersen, Nico  and
      Camus, Leon  and
      Sun, Zhifan  and
      Nguyen, Ngoc Nhu Hao  and
      Zehner, Fabian  and
      Cong, Longwei  and
      Mehler, Alexander  and
      Drachsler, Hendrik",
    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-1.44/",
    pages = "624--634",
    ISBN = "979-8-89176-409-5",
    abstract = "Rubrics are the primary reference for manual scoring of constructed responses, and there is growing interest in their use in automated scoring methodologies. In this work, we propose Aspect-Grounded Rubric{--}Answer Alignment (AGRAA), a rubric-based end-to-end scoring framework that models rubric descriptors as latent aspect spaces. Concretely, rubric descriptors are represented as low-dimensional subspaces derived from contextualised transformer embeddings, and student responses are scored according to how strongly their representations align with these rubric-induced spaces relative to the residual space outside them. This formulation provides a geometrically grounded interpretation of rubric-based scoring while enabling end-to-end training with standard transformer encoders. We introduce three distinct architectural variants and evaluate them on multiple short-answer and essay scoring datasets. Across these tasks, AGRAA achieves predictive performance highly competitive with strong neural and feature-based baselines. In addition, the framework yields interpretable intermediate representations that expose which rubric-defined aspects contribute to scoring decisions, enabling decision-aligned explanations grounded in rubric descriptors."
}