  We study learning from user feedback for extractive question answering by
simulating feedback using supervised data. We cast the problem as contextual
bandit learning, and analyze the characteristics of several learning scenarios
with focus on reducing data annotation. We show that systems initially trained
on a small number of examples can dramatically improve given feedback from
users on model-predicted answers, and that one can use existing datasets to
deploy systems in new domains without any annotation, but instead improving the
system on-the-fly via user feedback.
