How do you handle long-term dependencies in sequence generation with transformer-based models

Question

Are there strategies or ways to handle long-term dependencies in sequence generation with transformer-based models?

Answer 1

Yes, there are strategies to handle long-term dependencies in sequence generation with transformer-based models. Four of the strategies are: 

• Attention Mechanism: It uses techniques like sparse attention or memory layers to extend the model's ability to remember distant tokens.
• Positional Encoding: Apply relative positional encoding (used in Transformer-XL) to retain context for longer sequences without fixed position limits.
• Recurrent Mechanism: It uses a recurrence mechanism like a reformer's chunked recurrence to carry information across segments.
• Hierarchical Approaches: You can break down sequences into smaller units and apply hierarchical attention for multi-level content understanding.

The strategies mentioned above will help you in handling long-term dependencies in sequence generation with transformer-based models.