@inproceedings{mayer-martins-etal-2026-boostedcats,
    title = "{B}oosted{C}ats at {BEA} 2026 Shared Task 1: What Makes a Word Hard to Learn? Modeling {L}1 Influence on {E}nglish Vocabulary Difficulty",
    author = "Mayer Martins, Jonas  and
      Huang, Zhuojing  and
      Herygers, Aaricia  and
      Beinborn, Lisa",
    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-1.74/",
    pages = "1047--1064",
    ISBN = "979-8-89176-409-5",
    abstract = "What makes a word difficult to learn, and how does the difficulty depend on the learner{'}s native language? We computationally model vocabulary difficulty for English learners whose first language is Spanish, German, or Chinese with gradient-boosted models trained on features related to a word{'}s familiarity (e.g., frequency), meaning, surface form, and cross-linguistic transfer. Using Shapley values, we determine the importance of each feature group. Word familiarity is the dominant feature group shared by all three languages. However, predictions for Spanish- and German-speaking learners rely additionally on orthographic transfer. This transfer mechanism is unavailable to Chinese learners, whose difficulty is shaped by a combination of familiarity and surface features alone. Our models provide interpretable, L1-tailored difficulty estimates that can be used to design vocabulary curricula."
}