@inproceedings{bestgen-2026-satlab,
    title = "{SATL}ab at {BEA} 2026 Shared Task 1: Predicting the Difficulty of {E}nglish Words for Three {L}1 Learners Using Primarily Psycholinguistic Features",
    author = "Bestgen, Yves",
    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-1.66/",
    pages = "985--991",
    ISBN = "979-8-89176-409-5",
    abstract = "This paper presents SATLab{'}s participation in the BEA 2026 shared task on predicting the difficulty of English words for L2 learners. The proposed system uses features mainly derived from word frequency lists, lexical norms, and psychometric data, which are input into a gradient boosting decision tree model. It outperformed the Baseline system but performed significantly worse than the top-performing teams. Feature contributions to model performance are analysed using gain scores and Spearman rank correlations, and a brief analysis of the most significant errors is provided."
}