Eye‐tracking data from reading provide a structured signal with a fine‐grained temporal resolution which closely follows the sequential structure of the text. It is highly correlated with the cognitive load associated with different stages of human, cognitive text processing. While eye‐tracking data have been extensively studied to understand human cognition, it has only recently been considered for Natural Language Processing (NLP). In this review, we provide a comprehensive overview of how gaze data are being used in data‐driven NLP, in particular for sequence labelling and sequence classification tasks. We argue that eye‐tracking may effectively counter one of the core challenges of machine‐learning‐based NLP: the scarcity of annotated data. We outline the recent advances in gaze‐augmented NLP to discuss how the gaze signal from human readers can be leveraged while also considering the potentials and limitations of this data source.
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