@inproceedings{katinskaia-etal-2025-estimation,
    title = "Estimation of Text Difficulty in the Context of Language Learning",
    author = "Katinskaia, Anisia  and
      Vu, Anh-Duc  and
      Hou, Jue  and
      Vanhatalo, Ulla  and
      Wu, Yiheng  and
      Yangarber, Roman",
    editor = {Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Ana{\"i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng},
    booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bea-1.43/",
    pages = "594--611",
    ISBN = "979-8-89176-270-1",
    abstract = "Easy language and text simplification are currently topical research questions, with important applications in many contexts, and with various approaches under active investigation, including prompt-based methods. The estimation of the level of difficulty of a text becomes a crucial challenge when the estimator is employed in a simplification workflow as a quality-control mechanism. It can act as a \textit{critic} in frameworks where it can guide other models, which are responsible for generating text at a specified level of difficulty, as determined by the user{'}s needs.We present our work in the context of simplified Finnish. We discuss problems in collecting corpora for training models for estimation of text difficulty, and our experiments with estimation models.The results of the experiments are promising: the models appear usable both for assessment and for deployment as a component in a larger simplification framework."
}