  Successful Artificial Intelligence systems often require numerous labeled
data to extract information from document images. In this paper, we investigate
the problem of improving the performance of Artificial Intelligence systems in
understanding document images, especially in cases where training data is
limited. We address the problem by proposing a novel finetuning method using
reinforcement learning. Our approach treats the Information Extraction model as
a policy network and uses policy gradient training to update the model to
maximize combined reward functions that complement the traditional
cross-entropy losses. Our experiments on four datasets using labels and expert
feedback demonstrate that our finetuning mechanism consistently improves the
performance of a state-of-the-art information extractor, especially in the
small training data regime.
