@inproceedings{hodivoianu-etal-2025-predicting,
title = "Predicting Total Reading Time Using {R}omanian Eye-Tracking Data",
author = "Hodivoianu, Anamaria and
Kuvshynova, Oleksandra and
Popovici, Filip and
Luca, Adrian and
Nisioi, Sergiu",
editor = "Acarturk, Cengiz and
Nasir, Jamal and
Can, Burcu and
Coltekin, Cagr{\i}",
booktitle = "Proceedings of the First International Workshop on Gaze Data and Natural Language Processing",
month = sep,
year = "2025",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, BULGARIA",
url = "https://aclanthology.org/2025.gaze4nlp-1.9/",
pages = "71--75",
abstract = "This work introduces the first Romanian eye-tracking dataset for reading and investigates methods for predicting word-level total reading times. We develop and compare a range of models, from traditional machine learning using handcrafted linguistic features to fine-tuned Romanian BERT architectures, demonstrating strong correlations between predicted and observed reading times. Additionally, we propose a lexical simplification pipeline that leverages these TRT predictions to identify and substitute complex words, enhancing text readability. Our approach is integrated into an interactive web tool, illustrating the practical benefits of combining cognitive signals with NLP techniques for Romanian {---} a language with limited resources in this area."
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<abstract>This work introduces the first Romanian eye-tracking dataset for reading and investigates methods for predicting word-level total reading times. We develop and compare a range of models, from traditional machine learning using handcrafted linguistic features to fine-tuned Romanian BERT architectures, demonstrating strong correlations between predicted and observed reading times. Additionally, we propose a lexical simplification pipeline that leverages these TRT predictions to identify and substitute complex words, enhancing text readability. Our approach is integrated into an interactive web tool, illustrating the practical benefits of combining cognitive signals with NLP techniques for Romanian — a language with limited resources in this area.</abstract>
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%0 Conference Proceedings
%T Predicting Total Reading Time Using Romanian Eye-Tracking Data
%A Hodivoianu, Anamaria
%A Kuvshynova, Oleksandra
%A Popovici, Filip
%A Luca, Adrian
%A Nisioi, Sergiu
%Y Acarturk, Cengiz
%Y Nasir, Jamal
%Y Can, Burcu
%Y Coltekin, Cagrı
%S Proceedings of the First International Workshop on Gaze Data and Natural Language Processing
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, BULGARIA
%C Varna, Bulgaria
%F hodivoianu-etal-2025-predicting
%X This work introduces the first Romanian eye-tracking dataset for reading and investigates methods for predicting word-level total reading times. We develop and compare a range of models, from traditional machine learning using handcrafted linguistic features to fine-tuned Romanian BERT architectures, demonstrating strong correlations between predicted and observed reading times. Additionally, we propose a lexical simplification pipeline that leverages these TRT predictions to identify and substitute complex words, enhancing text readability. Our approach is integrated into an interactive web tool, illustrating the practical benefits of combining cognitive signals with NLP techniques for Romanian — a language with limited resources in this area.
%U https://aclanthology.org/2025.gaze4nlp-1.9/
%P 71-75
Markdown (Informal)
[Predicting Total Reading Time Using Romanian Eye-Tracking Data](https://aclanthology.org/2025.gaze4nlp-1.9/) (Hodivoianu et al., Gaze4NLP 2025)
ACL
- Anamaria Hodivoianu, Oleksandra Kuvshynova, Filip Popovici, Adrian Luca, and Sergiu Nisioi. 2025. Predicting Total Reading Time Using Romanian Eye-Tracking Data. In Proceedings of the First International Workshop on Gaze Data and Natural Language Processing, pages 71–75, Varna, Bulgaria. INCOMA Ltd., Shoumen, BULGARIA.