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 in-tepretability, crucial in assessment contexts as well as achieve good empirical performance.
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