Focus of this work is the prediction of reading times as the task is customarily dealt with in literature: that is, by collecting eye-tracking data that are averaged and employed to train learning models. We start by observing that systems trained on average values are ill-suited for the prediction of the reading times for specific subjects, as they fail to account for individual variability and accurately analyze the reading gestures of specific reader groups, or to target specific user needs. To overcome such limitation, that is to predict the reading times for a specific subject, we propose a novel approach based on creating an embedding to compactly describe her/his fixations. Embeddings are used to individuate readers that share same or similar reading behavior from a reference corpus. Models are then trained on values averaged over this subset of similar readers. Experimental results indicate that the proposed approach consistently outperforms its corresponding variants, in which predictions of reading times for specific readers are based on data from all subjects rather than from the most similar ones.

Reading movements and times are a precious cue to follow reader’s strategy, and to track the underlying effort in text processing. To date, many approaches are being devised to simplify texts to overcome difficulties stemming from sentences obscure, ambiguous or deserving clarification. In the legal domain, ensuring the clarity of norms and regulations is of the utmost importance, as the full understanding of such documents lies at the foundation of core social obligations and rights. This task requires determining which utterances and text excerpts are difficult for which (sort of) reader. This investigation is the aim of the present work. We propose a preliminary study based on eye-tracking data of 61 readers, with focus on individuating different reader profiles, and on predicting reading times of our readers.