  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.
