Active Information Acquisition
This work addresses efficient information acquisition for specific tasks, but it is incremental as it builds on existing frameworks without introducing a new paradigm.
The authors tackled the problem of sequential and dynamic information acquisition for task-solving by proposing a framework under the Learning to Search paradigm, applying it to sentiment analysis and image recognition tasks with good statistical performance.
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.