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Predicting the Difficulty of Language Proficiency Tests

Beinborn, Lisa and Zesch, Torsten and Gurevych, Iryna (2014):
Predicting the Difficulty of Language Proficiency Tests.
In: Transactions of the Association for Computational Linguistics, pp. 517-529, 2, (1), [Online-Edition: http://aclweb.org/anthology/Q14-1040],
[Article]

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.

Item Type: Article
Erschienen: 2014
Creators: Beinborn, Lisa and Zesch, Torsten and Gurevych, Iryna
Title: Predicting the Difficulty of Language Proficiency Tests
Language: English
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.

Journal or Publication Title: Transactions of the Association for Computational Linguistics
Volume: 2
Number: 1
Uncontrolled Keywords: UKP_a_ENLP;reviewed;UKP_a_WALL;UKP_p_AutoExerGen
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
Date Deposited: 31 Dec 2016 14:29
Official URL: http://aclweb.org/anthology/Q14-1040
Identification Number: TUD-CS-2014-0976
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