@article{beinborn-etal-2014-predicting,
    title = "Predicting the Difficulty of Language Proficiency Tests",
    author = "Beinborn, Lisa  and
      Zesch, Torsten  and
      Gurevych, Iryna",
    editor = "Lin, Dekang  and
      Collins, Michael  and
      Lee, Lillian",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "2",
    year = "2014",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/ingest-emnlp/Q14-1040/",
    doi = "10.1162/tacl_a_00200",
    pages = "517--530",
    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."
}