@inproceedings{stearns-etal-2026-uogal,
    title = "uogal at {BEA} 2026 Shared Task 1: Ensemble of Multilingual Encoders with {NMT} Augmentation for {L}1-Aware Vocabulary Difficulty Prediction",
    author = "Stearns, Bernardo  and
      McCrae, John P.  and
      Gaillat, Thomas  and
      Kafunah, Jefkine",
    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-1.75/",
    pages = "1065--1076",
    ISBN = "979-8-89176-409-5",
    abstract = "We submit a system for the closed track of the BEA 2026 shared task on L1-aware vocabulary difficulty prediction (Spanish, German, Mandarin Chinese). We compared three families of approaches: hand-crafted tabular features with tree-based regressors, fine-tuned multilingual encoders, and decoder-based artificial learner simulation using LoRA-tuned Pythia models, each evaluated with and without NMT-augmented English context. Our best system is an ensemble of four base and four NMT-augmented multilingual encoders combined through per-language stacking (Nelder-Mead and ElasticNet meta-learner), which placed 2nd in the closed track across all three languages. We also report a monotonic scaling study of the decoder-based artificial learner simulation pipeline."
}