2021 EMNLP EMNLP 2021

Predicting Text Readability from Scrolling Interactions

Abstract

AbstractJudging 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.

🌉 Interdisciplinary Bridge — Data Science & Analytics and Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — scrolling behavior
🐣 Hot Topic Early Bird — text simplification
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio