Predicting the Difficulty of Language Proficiency Tests

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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Date Issued:
01.11.2014
URN:
urn:nbn:de:hbz:464-20211026-112114-9
DOI:
10.1162/tacl_a_00200
Language:
English
Type of Resource:
Text
Collection:
E-Publications
Subject categories of the Deutsche Nationalbibliografie:
004 Data processing Computer sciences
URL:
https://www.aclweb.org/anthology/Q14-1040
Hosting institution:
Faculty of Engineering, Computer Science and Applied Cognitive Science, Computer Science, Language Technology Lab
Info on primary publication:
Beinborn, L, Zesch, T, Gurevych, I: Predicting the Difficulty of Language Proficiency Tests. Transactions of the Association for Computational Linguistics (2014) 2: 517–530. https://doi.org/10.1162/tacl_a_00200

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©2014 Association for Computational Linguistics

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