Abstract
Automated scoring systems used for the evaluation of spoken or written responses in language assessments need to balance good empirical performance with the interpretability of the scoring models. We compare several methods of feature selection for such scoring systems and show that the use of shrinkage methods such as Lasso regression makes it possible to rapidly build models that both satisfy the requirements of validity and intepretability, crucial in assessment contexts as well as achieve good empirical performance.
Cite
CITATION STYLE
Loukina, A., Zechner, K., Chen, L., & Heilman, M. (2015). Feature selection for automated speech scoring. In 10th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2015 at the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 (pp. 12–19). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/w15-0602
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