  Eye movements are known to reflect cognitive processes in reading, and
psychological reading research has shown that eye gaze patterns differ between
readers with and without dyslexia. In recent years, researchers have attempted
to classify readers with dyslexia based on their eye movements using Support
Vector Machines (SVMs). However, these approaches (i) are based on highly
aggregated features averaged over all words read by a participant, thus
disregarding the sequential nature of the eye movements, and (ii) do not
consider the linguistic stimulus and its interaction with the reader's eye
movements. In the present work, we propose two simple sequence models that
process eye movements on the entire stimulus without the need of aggregating
features across the sentence. Additionally, we incorporate the linguistic
stimulus into the model in two ways -- contextualized word embeddings and
manually extracted linguistic features. The models are evaluated on a Mandarin
Chinese dataset containing eye movements from children with and without
dyslexia. Our results show that (i) even for a logographic script such as
Chinese, sequence models are able to classify dyslexia on eye gaze sequences,
reaching state-of-the-art performance, and (ii) incorporating the linguistic
stimulus does not help to improve classification performance.
