Data Programming using Continuous and Quality-Guided Labeling Functions
Table 5: Triggering of the continuous and discrete versions of functions LF1 and LF2 for the two example sentences S1 and S2.
['Sentence id', 'Discrete LF1 (+1)', 'Discrete LF2 (-1)', 'Continuous LF1 (+1)', 'Continuous LF2 (-1)']
For S1, in the discrete case, correct consensus can easily be performed to output the true label +1 as LF1 (designed for class +1) gets triggered whereas LF2 (designed for class -1) is not triggered. correct consensus is challenging since both LF1 and LF2 produce non-trivial scores. Let us say that we threhold the score of each continuous LF to be able to mimic the discrete case; e.g., score above the threshold will mean that the LF is triggered and otherwise, not. However, the threshold would depend on the particular LF (possibly 0.75 for LF1 and 0.84 for LF2), and is tricky to estimate in an unsupervised setup such as ours. In addition to modeling the triggering distribution of each LF (continuous or discrete), we also model the distribution of scores for each continuous LF.