Latent Semantic and Disentangled Attention
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces Bayesian clustered disentanglement for mask attention in transformers. It addresses redundancy and model capacity issues in attention mechanisms, improving sequence data representation.
Area Of Science
- Artificial Intelligence
- Natural Language Processing
- Machine Learning
Background
- Transformer models excel in sequential tasks due to multi-head self-attention.
- Current attention mechanisms suffer from information redundancy, limited model capacity, and lack of robustness.
Purpose Of The Study
- To propose a novel Bayesian semantic and disentangled mask attention mechanism.
- To address weaknesses in existing transformer attention frameworks, specifically redundancy and model uncertainty.
Main Methods
- Developed a Bayesian learning approach for clustered disentanglement.
- Implemented a mask optimized via semantic clustering to filter attention weights.
- Introduced latent topic information to compensate for redundant features.
Main Results
- The proposed Bayesian clustered disentanglement effectively reduces redundant features in sequence data representation.
- The method enhances model capacity by addressing similar attention weight patterns across heads.
- Experiments demonstrate improved performance in machine translation and speech recognition tasks.
Conclusions
- Bayesian clustered disentanglement for mask attention offers a robust solution to transformer weaknesses.
- The approach enhances semantic understanding and data representation in sequential learning.
- This method shows significant merit for applications like machine translation and speech recognition.
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