Learning How to Actively Learn: A Deep Imitation Learning Approach
Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary. We introduce a method that learns an AL {``}policy{''} using {``}imitation learning{''} (IL). Our IL-based approach makes use of an efficient and effective {``}algorithmic expert{''}, which provides the policy learner with good actions in the encountered AL situations. The AL strategy is then learned with a feedforward network, mapping situations to most informative query datapoints. We evaluate our method on two different tasks: text classification and named entity recognition. Experimental results show that our IL-based AL strategy is more effective than strong previous methods using heuristics and reinforcement learning.
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