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        <title>Supervised and Unsupervised Neural Approaches to Text Readability</title>
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        <namePart type="given">Matej</namePart>
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    <name type="personal">
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    <abstract>We present a set of novel neural supervised and unsupervised approaches for determining the readability of documents. In the unsupervised setting, we leverage neural language models, whereas in the supervised setting, three different neural classification architectures are tested. We show that the proposed neural unsupervised approach is robust, transferable across languages, and allows adaptation to a specific readability task and data set. By systematic comparison of several neural architectures on a number of benchmark and new labeled readability data sets in two languages, this study also offers a comprehensive analysis of different neural approaches to readability classification. We expose their strengths and weaknesses, compare their performance to current state-of-the-art classification approaches to readability, which in most cases still rely on extensive feature engineering, and propose possibilities for improvements.</abstract>
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    <identifier type="doi">10.1162/coli_a_00398</identifier>
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        <date>2021-03</date>
        <detail type="volume"><number>47</number></detail>
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            <start>141</start>
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%0 Journal Article
%T Supervised and Unsupervised Neural Approaches to Text Readability
%A Martinc, Matej
%A Pollak, Senja
%A Robnik-Šikonja, Marko
%J Computational Linguistics
%D 2021
%8 March
%V 47
%N 1
%I MIT Press
%C Cambridge, MA
%F martinc-etal-2021-supervised
%X We present a set of novel neural supervised and unsupervised approaches for determining the readability of documents. In the unsupervised setting, we leverage neural language models, whereas in the supervised setting, three different neural classification architectures are tested. We show that the proposed neural unsupervised approach is robust, transferable across languages, and allows adaptation to a specific readability task and data set. By systematic comparison of several neural architectures on a number of benchmark and new labeled readability data sets in two languages, this study also offers a comprehensive analysis of different neural approaches to readability classification. We expose their strengths and weaknesses, compare their performance to current state-of-the-art classification approaches to readability, which in most cases still rely on extensive feature engineering, and propose possibilities for improvements.
%R 10.1162/coli_a_00398
%U https://aclanthology.org/2021.cl-1.6
%U https://doi.org/10.1162/coli_a_00398
%P 141-179

Markdown (Informal)

[Supervised and Unsupervised Neural Approaches to Text Readability](https://aclanthology.org/2021.cl-1.6) (Martinc et al., CL 2021)

• Supervised and Unsupervised Neural Approaches to Text Readability (Martinc et al., CL 2021)

ACL

• Matej Martinc, Senja Pollak, and Marko Robnik-Šikonja. 2021. Supervised and Unsupervised Neural Approaches to Text Readability. Computational Linguistics, 47(1):141–179.