@inproceedings{giouli-etal-2026-calibrated,
    title = "A Calibrated and Interpretable Framework for Multilingual Text Difficulty Prediction",
    author = "Giouli, Voula  and
      Tsoulouhas, George  and
      Sioupi, Athina  and
      Michalopoulou, Stamatia",
    editor = "Di Nunzio, Giorgio Maria  and
      Vezzani, Federica  and
      Ermakova, Liana  and
      Azarbonyad, Hosein  and
      Kamps, Jaap",
    booktitle = "Proceedings of the 2nd Workshop on Evaluating Text Difficulty in a Multilingual Context ({D}e{T}erm{I}t! 2026)",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/ingest-evalita/2026.determit-1.7/",
    doi = "10.63317/3cmykny29qkb",
    pages = "63--73",
    abstract = "Text difficulty prediction in educational contexts requires models that balance predictive performance, interpretability, calibration, and pedagogical alignment. While transformer-based approaches increasingly dominate text difficulty classification, educational applications demand transparent and linguistically grounded modeling. This paper presents work aimed at developing a workbench for CEFR-based text difficulty prediction. The proposed platform comprises three main components: (i) a tool for CEFR-aligned dataset preparation incorporating a pipeline for documenting, processing, and enriching textual data, (ii) CEFR-aligned datasets, and (iii) three alternative modeling approaches, namely a rule-based baseline, a feature-based Machine Learning (ML) classifier, and a fine-tuned BERT model. Our approach integrates linguistically informed feature engineering with data-driven modeling techniques, thereby balancing transparency and predictive performance. The proposed workbench has been designed as a language-agnostic infrastructure that can be extended to any language. In its current implementation, it has been applied to the creation of a German CEFR dataset, while its Greek counterpart is currently under development."
}