  We propose a novel interactive learning framework which we refer to as
Interactive Attention Learning (IAL), in which the human supervisors
interactively manipulate the allocated attentions, to correct the model's
behavior by updating the attention-generating network. However, such a model is
prone to overfitting due to scarcity of human annotations, and requires costly
retraining. Moreover, it is almost infeasible for the human annotators to
examine attentions on tons of instances and features. We tackle these
challenges by proposing a sample-efficient attention mechanism and a
cost-effective reranking algorithm for instances and features. First, we
propose Neural Attention Process (NAP), which is an attention generator that
can update its behavior by incorporating new attention-level supervisions
without any retraining. Secondly, we propose an algorithm which prioritizes the
instances and the features by their negative impacts, such that the model can
yield large improvements with minimal human feedback. We validate IAL on
various time-series datasets from multiple domains (healthcare, real-estate,
and computer vision) on which it significantly outperforms baselines with
conventional attention mechanisms, or without cost-effective reranking, with
substantially less retraining and human-model interaction cost.
