@inproceedings{dalbo-2026-uga,
    title = "{UGA} Threshold at {BEA} 2026 Shared Task 1: Predicting Vocabulary Acquisition Difficulty with Hand-Crafted {SLA}-Based Features",
    author = "Dalbo, Emma",
    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-1.67/",
    pages = "992--996",
    ISBN = "979-8-89176-409-5",
    abstract = "This paper describes a feature-based system submitted to the BEA 2026 Shared Task on Vocabulary Difficulty Prediction (closed track). The system models vocabulary difficulty for English learners using linguistically motivated features capturing frequency, cross-linguistic similarity, phonological and orthographic complexity, and semantic properties, supplemented by multilingual embeddings (reduced via PCA). Multiple regression models were evaluated using cross-validation, with final predictions generated from ensemble and single-model configurations per language.The system achieves competitive performance across all three L1 groups (German, Spanish, and Chinese), outperforming the XLM-RoBERTa baseline in seven of nine runs in terms of RMSE, with the strongest gains observed for Chinese and more modest improvements for Spanish. An ablation study further demonstrates that frequency and cross-linguistic similarity factors contribute most substantially to predictive performance, with effects varying across L1s. These findings highlight the role of interpretable linguistic features in modeling vocabulary difficulty in an L1-aware setting."
}