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        <title>Semi-Supervised Joint Estimation of Word and Document Readability</title>
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        <namePart type="given">Yoshinari</namePart>
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            <title>Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)</title>
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        <name type="personal">
            <namePart type="given">Alexander</namePart>
            <namePart type="family">Panchenko</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2021.textgraphs-1.16</identifier>
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%0 Conference Proceedings
%T Semi-Supervised Joint Estimation of Word and Document Readability
%A Fujinuma, Yoshinari
%A Hagiwara, Masato
%Y Panchenko, Alexander
%Y Malliaros, Fragkiskos D.
%Y Logacheva, Varvara
%Y Jana, Abhik
%Y Ustalov, Dmitry
%Y Jansen, Peter
%S Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)
%D 2021
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F fujinuma-hagiwara-2021-semi
%X 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.
%R 10.18653/v1/2021.textgraphs-1.16
%U https://aclanthology.org/2021.textgraphs-1.16
%U https://doi.org/10.18653/v1/2021.textgraphs-1.16
%P 150-155

Markdown (Informal)

[Semi-Supervised Joint Estimation of Word and Document Readability](https://aclanthology.org/2021.textgraphs-1.16) (Fujinuma & Hagiwara, TextGraphs 2021)

• Semi-Supervised Joint Estimation of Word and Document Readability (Fujinuma & Hagiwara, TextGraphs 2021)

ACL

• Yoshinari Fujinuma and Masato Hagiwara. 2021. Semi-Supervised Joint Estimation of Word and Document Readability. In Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15), pages 150–155, Mexico City, Mexico. Association for Computational Linguistics.