@inproceedings{latif-etal-2026-nlp,
    title = "{NLP}-Explorers at {BEA} 2026 Shared Task 1: {D}e{BERT}a{--}{C}at{B}oost Weighted Ensemble Approach for {L}1-Specific Vocabulary Difficulty Prediction",
    author = "Latif, Tayyab  and
      Bibi, Asifa  and
      Butt, Sabur  and
      Sidorov, Grigori  and
      Gelbukh, Alexander",
    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-1.78/",
    pages = "1106--1112",
    ISBN = "979-8-89176-409-5",
    abstract = "Vocabulary difficulty prediction aims to estimate how difficult a word is for a learner. This is an important problem because word difficulty is shaped not only by the word itself, but also by the learner{'}s background and the context in which the word appears. In this work, we predict continuous difficulty scores for English target words using learnerspecific information. Our approach combines a fine-tuned DeBERTa v3 Large model with a CatBoost regressor trained on transformer-based embeddings. The final score is produced through weighted ensembling, where DeBERTa provides the main prediction and CatBoost adds a smaller complementary signal. Our final system achieved RMSE scores of 1.040 for Spanish, 0.992 for German, and 0.882 for Chinese. The results were also stable across multiple runs, showing that the model behaved consistently under small changes in ensemble weight. These findings show that a simple hybrid system can provide reliable performance for vocabulary difficulty prediction. They also suggest that combining strong contextual representations with a lightweight regression model is an effective way to model learner-sensitive word difficulty."
}