@inproceedings{scozzaro-etal-2026-cervino,
title = "{CERVINO}: A Large-Scale Eye-Tracking Resource of {I}talian Legal Texts",
author = "Scozzaro, Calogero Jerik and
Delsanto, Matteo and
Pasquet, Sara and
Revelli, Luisa and
Mastropaolo, Antonio and
Radicioni, Daniele P.",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2435/",
pages = "55269--55283",
ISBN = "979-8-89176-459-0",
abstract = "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."
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%0 Conference Proceedings
%T CERVINO: A Large-Scale Eye-Tracking Resource of Italian Legal Texts
%A Scozzaro, Calogero Jerik
%A Delsanto, Matteo
%A Pasquet, Sara
%A Revelli, Luisa
%A Mastropaolo, Antonio
%A Radicioni, Daniele P.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F scozzaro-etal-2026-cervino
%X 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.
%U https://aclanthology.org/2026.emnlp-main.2435/
%P 55269-55283
Markdown (Informal)
[CERVINO: A Large-Scale Eye-Tracking Resource of Italian Legal Texts](https://aclanthology.org/2026.emnlp-main.2435/) (Scozzaro et al., EMNLP 2026)
ACL
- Calogero Jerik Scozzaro, Matteo Delsanto, Sara Pasquet, Luisa Revelli, Antonio Mastropaolo, and Daniele P. Radicioni. 2026. CERVINO: A Large-Scale Eye-Tracking Resource of Italian Legal Texts. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 55269–55283, Budapest, Hungary. Association for Computational Linguistics.