@inproceedings{kanishcheva-kopotev-2026-automated,
    title = "Automated {CEFR}-Level Assignment for {U}krainian Texts",
    author = "Kanishcheva, Olha  and
      Kopotev, Mikhail",
    editor = "Romanyshyn, Mariana",
    booktitle = "Proceedings of the Fifth {U}krainian Natural Language Processing Conference ({UNLP} 2026)",
    month = may,
    year = "2026",
    address = "Lviv, Ukraine",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2026-06/2026.unlp-1.18/",
    pages = "209--222",
    ISBN = "979-8-89176-359-3",
    abstract = "The present study evaluates CEFR-based text complexity for Ukrainian using a new dataset compiled from textbooks, designed for language learners. We compare traditional machine learning, transformer-based models, and LLM-based evaluation across A1{--}B2 language proficiency levels. Results show that explicit linguistic features remain highly effective: a Random Forest classifier achieves the highest macro-F1 (0.576), slightly outperforming fine-tuned XLM-RoBERTa (0.574). While GPT-5.5 shows strong performance (macro-F1 0.564), marking a significant advancement over GPT-4.1, supervised models achieve slightly better scores in this experiment for the proficiency-level assessment. These findings suggest that structured linguistic analysis is a robust alternative to purely neural approaches for Ukrainian CEFR classification."
}