A systematic review of eye-tracking data in NLP: exploring low-cost and cross-lingual possibilities
by Alba Haveriku; Hakik Paci; Nelda Kote; Paola Shasivari; Elinda Kajo Meçe
International Journal of Grid and Utility Computing (IJGUC), Vol. 16, No. 1, 2025

Abstract: Integrating eye-tracking data into text processing models is consistently demonstrating improvements in their outcomes. Numerous studies have been undertaken to explore low-cost alternatives and investigate cross-lingual possibilities. In our systematic literature review, we provide an overview of the related studies, based on four main dimensions: eye-tracking in Natural Language Processing (NLP) subfields, cross-lingual eye-tracking, most relevant eye-tracking devices and low-cost eye-tracking opportunities. We highlight key studies showcasing that integrating eye-tracking data during training or testing improves the accuracy of NLP models in diverse subfields. There is a necessity to analyse eye-tracking data across different languages to explore cross-lingual patterns and variations. Furthermore, eye-tracking devices vary in form, sampling rate, accuracy and costs. Notably, low-cost devices are demonstrating acceptable accuracy rates, paving the way for a potentially cost-effective future in conducting eye-tracking experiments.

Online publication date: Sun, 12-Jan-2025

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