ClarQ: A large-scale and diverse dataset for Clarification Question Generation
Table 2: Performance of the classifier on the annotated test set at the end of each iteration of the up-sampling procedure.
['[BOLD] Iteration', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1']
It can be clearly observed that recall of the classifier increases with each iteration, although the final recall (i.e at iteration 5) is lower than the recall obtained in the first iteration of the down-sampling process. Given that there are a large number of (p,q) tuples, a drop in recall will not hamper the quality nor the diversity of the dataset. At the end of the process, we also observe that there is only a marginal drop in precision. Thus, at the end of the last iteration we are able to obtain a classifier which has a high precision and a reasonable recall.