Attention Is All You Need
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.
- The paper addresses sequence transduction tasks that were previously handled mainly by recurrent or convolutional neural networks, which can be complex and harder to parallelize. - It introduces the Transformer, a simpler architecture built entirely on attention mechanisms, removing the need for recurrence and convolutions. - The key result is that sequence modeling can be done without traditional sequential components, showing that attention alone is sufficient for this class of problems. - This matters because the architecture is simpler and more efficient to train, and it helped establish a new foundation for modern natural language processing models.