@inproceedings{mattu-etal-2026-saakth,
    title = "{SAAKTH} at {BEA} 2026 Shared Task 1: {L}1-Aware {E}nglish Vocabulary Difficulty Prediction with Hybrid Transformer and Psycholinguistic Features",
    author = "Mattu, Karthik  and
      Dhall, Adit  and
      Naguru, Arshad  and
      Sehgal, Shubh  and
      Gowda, Thejas  and
      Sung, Hakyung",
    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-1.69/",
    pages = "1010--1015",
    ISBN = "979-8-89176-409-5",
    abstract = "This paper presents team SAAKTH{'}s system for the BEA 2026 Shared Task on Vocabulary Difficulty Prediction (Closed Track). We address the key challenge that English word difficulty is not fixed but varies with English learners' native language. Our approach combines a fine-tuned XLM-RoBERTa-large encoder with handcrafted psycholinguistic features engineered separately for each L1 group. These features are integrated via a shallow multilayer perceptron and optimized separately per L1, with five-seed ensembling and XGBoost-based blending for stability. Our system achieves RMSEs of 0.997 (es), 1.002 (de), and 0.932 (cn) on the development set, improving 20{--}25{\%} over the baseline. Results highlight the effectiveness of L1-aware modeling under limited data."
}