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    <titleInfo>
        <title>Predicting the Difficulty of Language Proficiency Tests</title>
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    <name type="personal">
        <namePart type="given">Lisa</namePart>
        <namePart type="family">Beinborn</namePart>
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    <name type="personal">
        <namePart type="given">Iryna</namePart>
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    <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>
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        <url>https://aclanthology.org/Q14-1040</url>
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    <part>
        <date>2014</date>
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            <start>517</start>
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%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.