Attention Is All You Need

Key Takeaways

• Self-attention can capture long-range dependencies more effectively than RNNs
• The transformer architecture's parallelizability makes it much more efficient to train
• Multi-head attention allows the model to focus on different aspects simultaneously
• Position encoding is crucial for sequence understanding without recurrence