Weakly supervised approaches have gained popularity in the last two years, but there is still a significant amount of overhead in applying these methods to more complex NLP tasks. The performance of weakly supervised systems is contingent on both the quality and quantity of independent sources of weak signals- if a practitioner cannot come up with sufficient sources themselves then weak supervision is largely impractical.

To overcome this, we can use techniques to interactively generate candidate sources of weak supervision to guide the practitioner, making weak supervision practical for many tasks that would otherwise be difficult to support.

In this tutorial, we’ll first build a basic weakly supervised system for an NLP task, and then augment it with some of these generative techniques to speed up the iterative process.