Optimizing Attention for Sequence Modeling via Reinforcement Learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Natural Language Processing

Background:

• Attention mechanisms are crucial for sequence modeling in deep learning.
• Current attention methods sometimes produce non-intuitive weight distributions in tasks like machine translation and sentiment analysis.

Purpose of the Study:

• To develop a deep reinforcement learning approach for optimizing attention distribution.
• To enhance the interpretability of attention mechanisms in deep learning models.

Main Methods:

• Utilized deep reinforcement learning (DRL) to iteratively adjust attention weights.
• Trained the DRL agent to minimize end-task training losses.
• Applied the method to various attention networks and downstream tasks.

Main Results:

• Achieved significant improvements in end-task performance across different tasks and attention architectures.
• Demonstrated the generation of more intuitive and reasonable attention weight distributions.
• Showcased the ability of the retrofitting method to enhance the explainability of baseline attention models.

Conclusions:

• Deep reinforcement learning offers an effective strategy for optimizing attention mechanisms in deep learning.
• The proposed method not only boosts performance but also enhances the interpretability of attention, aiding in understanding model behavior.