2021/04/27 by Yoshinari Fujinuma, Fujinuma, Yoshinari, Masato Hagiwara +1 · 1 citation
Computer Science · Social Sciences · #Computation and Language (cs.CL) #Digital Accessibility for Disabilities #FOS: Computer and information sciences #Text Readability and Simplification
paper · pdf · doi:10.48550/arxiv.2104.13103
openalex publication_date 2021/04/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Readability or difficulty estimation of words and documents has been investigated independently in the literature, often assuming the existence of extensive annotated resources for the other. Motivated by our analysis showing that there is a recursive relationship between word and document difficulty, we propose to jointly estimate word and document difficulty through a graph convolutional network (GCN) in a semi-supervised fashion. Our experimental results reveal that the GCN-based method can achieve higher accuracy than strong baselines, and stays robust even with a smaller amount of labeled data.