Attention in Reasoning: Dataset, Analysis, and Modeling.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Attention mechanisms are widely used in deep neural networks for interpretability and performance enhancement.
• Limited research has explored the progression and rationality of attention in task accomplishment.

Purpose of the Study:

• To introduce a novel framework, Attention with Reasoning capability (AiR), for analyzing and improving attention mechanisms in deep learning models.
• To quantitatively measure attention's reasoning process and its impact on task performance.

Main Methods:

• Defined an evaluation metric based on atomic reasoning operations to quantify attention's reasoning process.
• Collected human eye-tracking and answer correctness data for analysis.
• Analyzed machine and human attention mechanisms for reasoning capabilities and task performance impact.
• Proposed progressive supervision of attention learning and differentiation of correct/incorrect attention patterns.

Main Results:

• Demonstrated the effectiveness of the AiR framework in analyzing attention mechanisms.
• Showcased improvements in reasoning capability and task performance of visual question answering models.
• Validated the proposed methods for supervising attention learning and differentiating attention patterns.

Conclusions:

• The AiR framework provides a robust method for evaluating and enhancing attention mechanisms in deep learning.
• Progressive supervision and pattern differentiation significantly improve model reasoning and performance.
• This work contributes to more interpretable and effective deep learning models.