<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="beinborn-etal-2014-predicting">
    <titleInfo>
        <title>Predicting the Difficulty of Language Proficiency Tests</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Lisa</namePart>
        <namePart type="family">Beinborn</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Torsten</namePart>
        <namePart type="family">Zesch</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Iryna</namePart>
        <namePart type="family">Gurevych</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2014</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <genre authority="bibutilsgt">journal article</genre>
    <relatedItem type="host">
        <titleInfo>
            <title>Transactions of the Association for Computational Linguistics</title>
        </titleInfo>
        <originInfo>
            <issuance>continuing</issuance>
            <publisher>MIT Press</publisher>
            <place>
                <placeTerm type="text">Cambridge, MA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">periodical</genre>
        <genre authority="bibutilsgt">academic journal</genre>
    </relatedItem>
    <abstract>Language proficiency tests are used to evaluate and compare the progress of language learners. We present an approach for automatic difficulty prediction of C-tests that performs on par with human experts. On the basis of detailed analysis of newly collected data, we develop a model for C-test difficulty introducing four dimensions: solution difficulty, candidate ambiguity, inter-gap dependency, and paragraph difficulty. We show that cues from all four dimensions contribute to C-test difficulty.</abstract>
    <identifier type="citekey">beinborn-etal-2014-predicting</identifier>
    <identifier type="doi">10.1162/tacl_a_00200</identifier>
    <location>
        <url>https://aclanthology.org/Q14-1040/</url>
    </location>
    <part>
        <date>2014</date>
        <detail type="volume"><number>2</number></detail>
        <extent unit="page">
            <start>517</start>
            <end>530</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Journal Article
%T Predicting the Difficulty of Language Proficiency Tests
%A Beinborn, Lisa
%A Zesch, Torsten
%A Gurevych, Iryna
%J Transactions of the Association for Computational Linguistics
%D 2014
%V 2
%I MIT Press
%C Cambridge, MA
%F beinborn-etal-2014-predicting
%X Language proficiency tests are used to evaluate and compare the progress of language learners. We present an approach for automatic difficulty prediction of C-tests that performs on par with human experts. On the basis of detailed analysis of newly collected data, we develop a model for C-test difficulty introducing four dimensions: solution difficulty, candidate ambiguity, inter-gap dependency, and paragraph difficulty. We show that cues from all four dimensions contribute to C-test difficulty.
%R 10.1162/tacl_a_00200
%U https://aclanthology.org/Q14-1040/
%U https://doi.org/10.1162/tacl_a_00200
%P 517-530

Markdown (Informal)

[Predicting the Difficulty of Language Proficiency Tests](https://aclanthology.org/Q14-1040/) (Beinborn et al., TACL 2014)

• Predicting the Difficulty of Language Proficiency Tests (Beinborn et al., TACL 2014)

ACL

• Lisa Beinborn, Torsten Zesch, and Iryna Gurevych. 2014. Predicting the Difficulty of Language Proficiency Tests. Transactions of the Association for Computational Linguistics, 2:517–530.