  While data selection methods have been studied extensively in active
learning, data pruning, and data augmentation settings, there is little
evidence for the efficacy of these methods in industry scale settings,
particularly in low-resource languages. Our work presents ways of assessing
prospective training examples in those settings for their "usefulness" or
"difficulty". We also demonstrate how these measures can be used in selecting
important examples for training supervised machine learning models. We
primarily experiment with entropy and Error L2-Norm (EL2N) scores. We use these
metrics to curate high quality datasets from a large pool of \textit{Weak
Signal Labeled} data, which assigns no-defect high confidence hypotheses during
inference as ground truth labels. We then conduct training data augmentation
experiments using these de-identified datasets and demonstrate that score-based
selection can result in a 2% decrease in semantic error rate and 4%-7% decrease
in domain classification error rate when compared to the baseline technique of
random selection.
