Eye-Tracking (ET) data provide valuable insights into human language processing and have recently been employed across a wide range of fields, from psycholinguistics to NLP. In this work, we introduce CERVINO, the largest Italian eye-tracking-while-reading dataset to date, comprising data from more than 200 participants reading legal texts, recorded at 120Hz. Our analyses show that the dataset captures established psycholinguistic effects: reading times are influenced by word length, lexical frequency, and surprisal. We further evaluate the usefulness of the dataset in downstream tasks by fine-tuning BERT on ET measures. Results show improved alignment between model attention and human reading behavior while preserving downstream task performance. Moreover, combining our data with higher-frequency ET resources yields the strongest alignment results, suggesting that lower-frequency ET data provide useful cognitive signals. Overall, these findings indicate that lower-frequency ET data retain sufficient cognitive information to provide useful supervision for attention alignment and complement existing high-frequency resources.

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.