Abstract
Language proficiency tests are a useful tool for evaluating learner progress, if the test difficulty fits the level of the learner. In this work, we describe a generalized framework for test difficulty prediction that is applicable to several languages and test types. In addition, we develop two ranking strategies for candidate evaluation inspired by automatic solving methods based on language model probability and semantic relatedness. These ranking strategies lead to significant improvements for the difficulty prediction of cloze tests.
Cite
CITATION STYLE
Beinborn, L., Zesch, T., & Gurevych, I. (2015). Candidate evaluation strategies for improved difficulty prediction of language tests. In 10th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2015 at the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 (pp. 1–11). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/w15-0601
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