Posted by Sian Gooding, Intern, Google Research
Illiteracy affects at least 773 million people globally, both young and old. For these individuals, reading information from unfamiliar sources or on unfamiliar topics can be extremely difficult. Unfortunately, these inequalities have been further magnified by the global pandemic as a result of unequal access to education in reading and writing. In fact, UNESCO reports that over 100 million children are falling behind the minimum proficiency level in reading due to COVID-related school closures.
With increasing world-wide access to technology, reading on a device, such as a tablet or phone, has largely taken the place of traditional formats. This provides a unique opportunity to observe reading interactions, e.g., how a reader scrolls through a text, which can inform our understanding of what can make text difficult to read. This understanding is crucial when designing educational applications for low-proficiency readers and language learners, because it can be used to match learners with appropriately leveled texts as well as to support readers in understanding texts beyond their reading level.
In “Predicting Text Readability from Scrolling Interactions”, presented at CoNLL 2021, we show that data from on-device reading interactions can be used to predict how readable a text is. This novel approach provides insights into subjective readability — whether an individual reader has found a text accessible — and demonstrates that existing readability models can be improved by including feedback from scroll-based reading interactions. In order to encourage research in this area and to help enable more personalized tools for language learning and text simplification, we are releasing the dataset of reading interactions generated from our scrolling behavior–based readability assessment of English-language texts.
Understanding Text Difficulty
There are multiple aspects of a text that impact how difficult it is to read, including the vocabulary level, the syntactic structure, and overall coherence. Traditional machine learning approaches to
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