<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gooding-etal-2021-predicting">
    <titleInfo>
        <title>Predicting Text Readability from Scrolling Interactions</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Sian</namePart>
        <namePart type="family">Gooding</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yevgeni</namePart>
        <namePart type="family">Berzak</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tony</namePart>
        <namePart type="family">Mak</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Matt</namePart>
        <namePart type="family">Sharifi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 25th Conference on Computational Natural Language Learning</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Arianna</namePart>
            <namePart type="family">Bisazza</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Omri</namePart>
            <namePart type="family">Abend</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Judging the readability of text has many important applications, for instance when performing text simplification or when sourcing reading material for language learners. In this paper, we present a 518 participant study which investigates how scrolling behaviour relates to the readability of English texts. We make our dataset publicly available and show that (1) there are statistically significant differences in the way readers interact with text depending on the text level, (2) such measures can be used to predict the readability of text, and (3) the background of a reader impacts their reading interactions and the factors contributing to text difficulty.</abstract>
    <identifier type="citekey">gooding-etal-2021-predicting</identifier>
    <identifier type="doi">10.18653/v1/2021.conll-1.30</identifier>
    <location>
        <url>https://aclanthology.org/2021.conll-1.30</url>
    </location>
    <part>
        <date>2021-11</date>
        <extent unit="page">
            <start>380</start>
            <end>390</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Predicting Text Readability from Scrolling Interactions
%A Gooding, Sian
%A Berzak, Yevgeni
%A Mak, Tony
%A Sharifi, Matt
%Y Bisazza, Arianna
%Y Abend, Omri
%S Proceedings of the 25th Conference on Computational Natural Language Learning
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F gooding-etal-2021-predicting
%X Judging the readability of text has many important applications, for instance when performing text simplification or when sourcing reading material for language learners. In this paper, we present a 518 participant study which investigates how scrolling behaviour relates to the readability of English texts. We make our dataset publicly available and show that (1) there are statistically significant differences in the way readers interact with text depending on the text level, (2) such measures can be used to predict the readability of text, and (3) the background of a reader impacts their reading interactions and the factors contributing to text difficulty.
%R 10.18653/v1/2021.conll-1.30
%U https://aclanthology.org/2021.conll-1.30
%U https://doi.org/10.18653/v1/2021.conll-1.30
%P 380-390

Markdown (Informal)

[Predicting Text Readability from Scrolling Interactions](https://aclanthology.org/2021.conll-1.30) (Gooding et al., CoNLL 2021)

• Predicting Text Readability from Scrolling Interactions (Gooding et al., CoNLL 2021)

ACL

• Sian Gooding, Yevgeni Berzak, Tony Mak, and Matt Sharifi. 2021. Predicting Text Readability from Scrolling Interactions. In Proceedings of the 25th Conference on Computational Natural Language Learning, pages 380–390, Online. Association for Computational Linguistics.