@inproceedings{willis-third-2026-amati,
    title = "{AMATI} at {BEA} 2026 Shared Task 2: Automatic Short Answer Grading with Inductive Logic Programming and a Large Language Model",
    author = "Willis, Alistair  and
      Third, Aisling",
    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-1.89/",
    pages = "1217--1223",
    ISBN = "979-8-89176-409-5",
    abstract = "We discuss the AMATI submission to the BEA 2026 Shared Task on Rubric-based Short Answer Scoring for German. Our neuro-symbolic system uses a combination of symbolic rules, automatically learned with a form of Inductive Logic Programming, and the Mistral-large language model. We wanted to investigate whether the combination would improve overall grading performance, while using the automatically induced symbolic rules for explainability, and the LLM for robustness. We find that the combination of approached resulted in improved overall performance for the 3-way task. However, including the symbolic rules did not improve upon Mistral{'}s performance in the 2-way test.This paper presents our approach to the unseen answers challenges. Our team finished 6th out of 9 in the 2-way challenge, and 5th out of 8 in the 3-way challenge. In the 3-way challenge, neither our symbolic system nor the use of Mistral alone would have placed higher than 6th of the 8 competitors, illustrating the improvement of the combined approach over either of the individual approaches."
}