We argue that building such predictive models can help us better evaluate performance of NLP components that cannot be distinguished based on F-score alone, and illustrate our approach by comparing the current interpretation component in the system to a new classifier trained on the evaluation data.
we computed sepa-rate confusion matrices for each student.