Predicting the Difficulty of Language Proficiency Tests
Published
2014-11-04
Lisa Beinborn
,
Torsten Zesch
,
Iryna Gurevych
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.
PDF (presented at NAACL 2015)