Predicting the difficulty of language proficiency tests
Transactions of the association for computational linguistics, 2014•direct.mit.edu
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.
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
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.
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