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Generative Modeling of Synthetic Eye-Tracking Data: NLP-based Approach with Recurrent Neural Networks

Abstract : This study explores a Machine Learning-based approach for generating synthetic eye-tracking data. In this respect, a novel application of Recurrent Neural Networks is experimented. Our approach is based on learning the sequence patterns of eye-tracking data. The key idea is to represent eye-tracking records as textual strings, which describe the sequences of fixations and saccades. The study therefore could borrow methods from the Natural Language Processing (NLP) domain for transforming the raw eye-tracking data. The NLP-based transformation is utilised to convert the high-dimensional eye-tracking data into an amenable representation for learning. Furthermore, the generative modeling could be implemented as a task of text generation. Our empirical experiments support further exploration and development of such NLP-driven approaches for the purpose of producing synthetic eye-tracking datasets for a variety of potential applications. Copyright \textcopyright 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved
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Contributeur : Louise DESSAIVRE Connectez-vous pour contacter le contributeur
Soumis le : jeudi 26 mai 2022 - 15:05:49
Dernière modification le : dimanche 21 août 2022 - 13:38:21


  • HAL Id : hal-03679393, version 1



M. Elbattah, Jean-Luc Guérin, R. Carette, Federica Cilia, Gilles Dequen. Generative Modeling of Synthetic Eye-Tracking Data: NLP-based Approach with Recurrent Neural Networks. Proceedings of the 12th International Joint Conference on Computational Intelligence, Nov 2020, Budapest, Unknown Region. pp.479--484. ⟨hal-03679393⟩



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