Attention in Reasoning: Dataset, Analysis, and Modeling

Shi Chen, Ming Jiang, Jinhui Yang, Qi Zhao

Abstract:

While attention has been an increasingly popular component in deep neural networks to both interpret and boost the
performance of models, little work has examined how attention progresses to accomplish a task and whether it is reasonable. In this
work, we propose an Attention with Reasoning capability (AiR) framework that uses attention to understand and improve the process
leading to task outcomes. We first define an evaluation metric based on a sequence of atomic reasoning operations, enabling a
quantitative measurement of attention that considers the reasoning process. We then collect human eye-tracking and answer
correctness data, and analyze various machine and human attention mechanisms on their reasoning capability and how they impact
task performance. To improve the attention and reasoning ability of visual question answering models, we propose to supervise the
learning of attention progressively along the reasoning process and to differentiate the correct and incorrect attention patterns. We
demonstrate the effectiveness of the proposed framework in analyzing and modeling attention with better reasoning capability and task
performance. The code and data are available at https://github.com/szzexpoi/AiR.