@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://aclanthology.org/2025.acl-short.5/",
doi = "10.18653/v1/2025.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."
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%0 Conference Proceedings
%T Automatic detection of dyslexia based on eye movements during reading in Russian
%A Laurinavichyute, Anna
%A Lopukhina, Anastasiya
%A Reich, David Robert
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-252-7
%F laurinavichyute-etal-2025-automatic
%X 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.
%R 10.18653/v1/2025.acl-short.5
%U https://aclanthology.org/2025.acl-short.5/
%U https://doi.org/10.18653/v1/2025.acl-short.5
%P 59-66
Markdown (Informal)
[Automatic detection of dyslexia based on eye movements during reading in Russian](https://aclanthology.org/2025.acl-short.5/) (Laurinavichyute et al., ACL 2025)
ACL