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.
CITATION STYLE
Fujinuma, Y., & Hagiwara, M. (2021). Semi-Supervised Joint Estimation of Word and Document Readability. In TextGraphs 2021 - Graph-Based Methods for Natural Language Processing, Proceedings of the 15th Workshop - in conjunction with the 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL 2021 (pp. 150–155). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.textgraphs-1.16
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