Predicting Text Readability from Scrolling Interactions

Sian Gooding, Yevgeni Berzak, Tony Mak, Matt Sharifi


Abstract
Judging the readability of text has many important applications, for instance when performing text simplification or when sourcing reading material for language learners. In this paper, we present a 518 participant study which investigates how scrolling behaviour relates to the readability of English texts. We make our dataset publicly available and show that (1) there are statistically significant differences in the way readers interact with text depending on the text level, (2) such measures can be used to predict the readability of text, and (3) the background of a reader impacts their reading interactions and the factors contributing to text difficulty.
Anthology ID:
2021.conll-1.30
Volume:
Proceedings of the 25th Conference on Computational Natural Language Learning
Month:
November
Year:
2021
Address:
Online
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
380–390
Language:
URL:
https://aclanthology.org/2021.conll-1.30
DOI:
10.18653/v1/2021.conll-1.30
Bibkey:
Cite (ACL):
Sian Gooding, Yevgeni Berzak, Tony Mak, and Matt Sharifi. 2021. Predicting Text Readability from Scrolling Interactions. In Proceedings of the 25th Conference on Computational Natural Language Learning, pages 380–390, Online. Association for Computational Linguistics.
Cite (Informal):
Predicting Text Readability from Scrolling Interactions (Gooding et al., CoNLL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.conll-1.30.pdf
Video:
 https://aclanthology.org/2021.conll-1.30.mp4
Code
 siangooding/readability_scroll
Data
Scroll Readability DatasetOneStopEnglishOneStopQA