Estimation of Text Difficulty in the Context of Language Learning

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 crucial when the estimator is employed inside a simplification workflow as a quality-control mechanism.It can act as a 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.