  We propose a general framework for sequential and dynamic acquisition of
useful information in order to solve a particular task. While our goal could in
principle be tackled by general reinforcement learning, our particular setting
is constrained enough to allow more efficient algorithms. In this paper, we
work under the Learning to Search framework and show how to formulate the goal
of finding a dynamic information acquisition policy in that framework. We apply
our formulation on two tasks, sentiment analysis and image recognition, and
show that the learned policies exhibit good statistical performance. As an
emergent byproduct, the learned policies show a tendency to focus on the most
prominent parts of each instance and give harder instances more attention
without explicitly being trained to do so.
