@inproceedings{fujinuma-hagiwara-2021-semi,
    title = "Semi-Supervised Joint Estimation of Word and Document Readability",
    author = "Fujinuma, Yoshinari  and
      Hagiwara, Masato",
    editor = "Panchenko, Alexander  and
      Malliaros, Fragkiskos D.  and
      Logacheva, Varvara  and
      Jana, Abhik  and
      Ustalov, Dmitry  and
      Jansen, Peter",
    booktitle = "Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)",
    month = jun,
    year = "2021",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.textgraphs-1.16/",
    doi = "10.18653/v1/2021.textgraphs-1.16",
    pages = "150--155",
    abstract = "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."
}