@inproceedings{laurinavichyute-etal-2025-automatic,
    title = "Automatic detection of dyslexia based on eye movements during reading in {R}ussian",
    author = "Laurinavichyute, Anna  and
      Lopukhina, Anastasiya  and
      Reich, David Robert",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.acl-short.5/",
    pages = "59--66",
    ISBN = "979-8-89176-252-7",
    abstract = "Dyslexia, a common learning disability, requires an early diagnosis. However, current screening tests are very time- and resource-consuming. We present an LSTM that aims to automatically classify dyslexia based on eye movements recorded during natural readingcombined with basic demographic information and linguistic features. The proposed model reaches an AUC of 0.93 and outperforms thestate-of-the-art model by 7 {\%}. We report several ablation studies demonstrating that the fixation features matter the most for classification."
}