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The availability of labelled data is one of the main limitations in machinelearning. We can alleviate this using weak supervision: a framework that usesexpert-defined rules $\boldsymbol{\lambda}$ to estimate probabilistic labels$p(y|\boldsymbol{\lambda})$ for the entire data set. These rules, however, aredependent on what experts know about the problem, and hence may be inaccurateor may fail to capture important parts of the problem-space. To mitigate this,we propose Active WeaSuL: an approach that incorporates active learning intoweak supervision. In Active WeaSuL, experts do not only define rules, but theyalso iteratively provide the true label for a small set of points where theweak supervision model is most likely to be mistaken, which are then used tobetter estimate the probabilistic labels. In this way, the weak labels providea warm start, which active learning then improves upon. We make twocontributions: 1) a modification of the weak supervision loss function, suchthat the expert-labelled data inform and improve the combination of weaklabels; and 2) the maxKL divergence sampling strategy, which determines forwhich data points expert labelling is most beneficial. Our experiments showthat when the budget for labelling data is limited (e.g. $\leq 60$ datapoints), Active WeaSuL outperforms weak supervision, active learning, andcompeting strategies, with only a handful of labelled data points. This makesActive WeaSuL ideal for situations where obtaining labelled data is difficult.

Active WeaSuL: Improving Weak Supervision with Active Learning