Candidate evaluation strategies for improved difficulty prediction of language tests

Beinborn, Lisa; Zesch, Torsten; Gurevych, Iryna

doi:10.3115/v1/W15-0601

Tagungsbeitrag 2015 CC BY-NC-SA 3.0

Veröffentlicht

Candidate evaluation strategies for improved difficulty prediction of language tests

Beinborn, Lisa ; Zesch, Torsten ; Gurevych, Iryna

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.

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Einordnung

Konferenz:: NAACL HLT 2015, The Tenth Workshop on Innovative Use of NLP for Building Educational Applications, June 4, 2015 Denver, Colorado, USA
Datum der Veröffentlichung:: 2015
URN:: urn:nbn:de:hbz:464-20211025-135548-1
DOI:: 10.3115/v1/W15-0601
Sprache:: Englisch
Ressourcentyp:: Text
Kollektion:: E-Publikationen
Sachgruppen der Deutschen Nationalbibliographie:: 004 Informatik
Einrichtung:: Fakultät für Ingenieurwissenschaften, Informatik und Angewandte Kognitionswissenschaft, Informatik, Sprachtechnologie
Abgrenzungspolitik:: Beinborn L, Zesch T, Gurevych I (2015) Candidate evaluation strategies for improved difficulty prediction of language tests. In: Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications, 2015, pp. 1-11. Association for Computational Linguistics. https://doi.org/10.3115/v1/W15-0601