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We propose a novel interactive learning framework which we refer to asInteractive Attention Learning (IAL), in which the human supervisorsinteractively manipulate the allocated attentions, to correct the model'sbehavior by updating the attention-generating network. However, such a model isprone to overfitting due to scarcity of human annotations, and requires costlyretraining. Moreover, it is almost infeasible for the human annotators toexamine attentions on tons of instances and features. We tackle thesechallenges by proposing a sample-efficient attention mechanism and acost-effective reranking algorithm for instances and features. First, wepropose Neural Attention Process (NAP), which is an attention generator thatcan update its behavior by incorporating new attention-level supervisionswithout any retraining. Secondly, we propose an algorithm which prioritizes theinstances and the features by their negative impacts, such that the model canyield large improvements with minimal human feedback. We validate IAL onvarious time-series datasets from multiple domains (healthcare, real-estate,and computer vision) on which it significantly outperforms baselines withconventional attention mechanisms, or without cost-effective reranking, withsubstantially less retraining and human-model interaction cost.

Cost-effective Interactive Attention Learning with Neural Attention Processes