@inproceedings{felice-skidmore-2026-findings,
    title = "Findings of the {BEA} 2026 Shared Task on Vocabulary Difficulty Prediction for {E}nglish Learners",
    author = "Felice, Mariano  and
      Skidmore, Lucy",
    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-1.65/",
    pages = "964--984",
    ISBN = "979-8-89176-409-5",
    abstract = "This paper reports findings from the BEA 2026 Shared Task on Vocabulary Difficulty Prediction for English Learners across three L1s (Spanish, German and Mandarin). The task featured open and closed tracks, using data from the British Council{'}s Knowledge-based Vocabulary Lists (KVL). Submissions were received from 23 teams employing diverse modelling approaches, including transformers, Large Language Models, feature-based approaches and ensembles. Results were evaluated using RMSE, with winning systems significantly exceeding the baseline and establishing new state-of-the-art benchmarks. This paper offers an examination of the participating systems, performance across tracks and L1s, and the factors that can affect prediction accuracy."
}