Data Programming using Continuous and Quality-Guided Labeling Functions
Table 7: Discriminative Classifier trained on data labeled using the generative models and evaluated on held-out test data.
['[EMPTY]', '[BOLD] Spouse P', '[BOLD] Spouse R', '[BOLD] Spouse F1', '[BOLD] CDR P', '[BOLD] CDR R', '[BOLD] CDR F1', '[BOLD] Sms P', '[BOLD] Sms R', '[BOLD] Sms F1']
Data Programming is hinged upon creation of probabilistic training labels for any discriminative model with a standard loss function. A takeaway of the following results is that, through the use of continuous LFs and quality guides, the discriminative model generalizes much better beyond the heuristics encoded in the LFs.