On-the-Job Learning with Bayesian Decision Theory
  Our goal is to deploy a high-accuracy system starting with zero training
examples. We consider an "on-the-job" setting, where as inputs arrive, we use
real-time crowdsourcing to resolve uncertainty where needed and output our
prediction when confident. As the model improves over time, the reliance on
crowdsourcing queries decreases. We cast our setting as a stochastic game based
on Bayesian decision theory, which allows us to balance latency, cost, and
accuracy objectives in a principled way. Computing the optimal policy is
intractable, so we develop an approximation based on Monte Carlo Tree Search.
We tested our approach on three datasets---named-entity recognition, sentiment
classification, and image classification. On the NER task we obtained more than
an order of magnitude reduction in cost compared to full human annotation,
while boosting performance relative to the expert provided labels. We also
achieve a 8% F1 improvement over having a single human label the whole set, and
a 28% F1 improvement over online learning.
