@inproceedings{willoughby-etal-2026-surreycts,
    title = "{S}urrey{CTS} at {BEA} 2026 Shared Task 1: Semantic Funnelling and Entropy-based Multilingual Lexical Difficulty Prediction",
    author = "Willoughby, Georgina  and
      Painter, Jordan  and
      Kanojia, Diptesh  and
      Wells, Emily  and
      Orasan, Constantin",
    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-1.70/",
    pages = "1016--1023",
    ISBN = "979-8-89176-409-5",
    abstract = "We describe the SurreyCTS system for the BEA 2026 shared task on lexical difficulty prediction. Our approach combines multilingual transformer encoders (RemBERT and COMET) with engineered linguistic features including semantic funnelling, lexical similarity, attention-derived signals, and language-aware representations. A weighted ensemble of the five strongest systems placed fifth among open-track teams, outperforming the open-track baseline across all three learner L1 groups (Spanish, German, and Chinese)."
}