  Imitation learning algorithms provide state-of-the-art results on many
structured prediction tasks by learning near-optimal search policies. Such
algorithms assume training-time access to an expert that can provide the
optimal action at any queried state; unfortunately, the number of such queries
is often prohibitive, frequently rendering these approaches impractical. To
combat this query complexity, we consider an active learning setting in which
the learning algorithm has additional access to a much cheaper noisy heuristic
that provides noisy guidance. Our algorithm, LEAQI, learns a difference
classifier that predicts when the expert is likely to disagree with the
heuristic, and queries the expert only when necessary. We apply LEAQI to three
sequence labeling tasks, demonstrating significantly fewer queries to the
expert and comparable (or better) accuracies over a passive approach.
