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    <titleInfo>
        <title>Measuring text readability with machine comprehension: a pilot study</title>
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    <name type="personal">
        <namePart type="given">Marc</namePart>
        <namePart type="family">Benzahra</namePart>
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    <name type="personal">
        <namePart type="given">François</namePart>
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            <title>Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications</title>
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        <name type="personal">
            <namePart type="given">Helen</namePart>
            <namePart type="family">Yannakoudakis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Claudia</namePart>
            <namePart type="family">Leacock</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Nitin</namePart>
            <namePart type="family">Madnani</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        </name>
        <name type="personal">
            <namePart type="given">Ildikó</namePart>
            <namePart type="family">Pilán</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Torsten</namePart>
            <namePart type="family">Zesch</namePart>
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    <abstract>This article studies the relationship between text readability indice and automatic machine understanding systems. Our hypothesis is that the simpler a text is, the better it should be understood by a machine. We thus expect to a strong correlation between readability levels on the one hand, and performance of automatic reading systems on the other hand. We test this hypothesis with several understanding systems based on language models of varying strengths, measuring this correlation on two corpora of journalistic texts. Our results suggest that this correlation is rather small that existing comprehension systems are far to reproduce the gradual improvement of their performance on texts of decreasing complexity.</abstract>
    <identifier type="citekey">benzahra-yvon-2019-measuring</identifier>
    <identifier type="doi">10.18653/v1/W19-4443</identifier>
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%0 Conference Proceedings
%T Measuring text readability with machine comprehension: a pilot study
%A Benzahra, Marc
%A Yvon, François
%Y Yannakoudakis, Helen
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Madnani, Nitin
%Y Pilán, Ildikó
%Y Zesch, Torsten
%S Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F benzahra-yvon-2019-measuring
%X This article studies the relationship between text readability indice and automatic machine understanding systems. Our hypothesis is that the simpler a text is, the better it should be understood by a machine. We thus expect to a strong correlation between readability levels on the one hand, and performance of automatic reading systems on the other hand. We test this hypothesis with several understanding systems based on language models of varying strengths, measuring this correlation on two corpora of journalistic texts. Our results suggest that this correlation is rather small that existing comprehension systems are far to reproduce the gradual improvement of their performance on texts of decreasing complexity.
%R 10.18653/v1/W19-4443
%U https://aclanthology.org/W19-4443
%U https://doi.org/10.18653/v1/W19-4443
%P 412-422

Markdown (Informal)

[Measuring text readability with machine comprehension: a pilot study](https://aclanthology.org/W19-4443) (Benzahra & Yvon, BEA 2019)

• Measuring text readability with machine comprehension: a pilot study (Benzahra & Yvon, BEA 2019)

ACL

• Marc Benzahra and François Yvon. 2019. Measuring text readability with machine comprehension: a pilot study. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 412–422, Florence, Italy. Association for Computational Linguistics.