"Attention Is All You Need" — The Transformer Architecture

This is a summary of the original Attention Is All You Need white paper, which can be found at this link.

The Transformer is a neural network that ditches recurrence and convolutions and instead uses only attention to understand and generate sequences. It trains faster, scales better, and set new state-of-the-art translation results while being cheaper to train.

Why This Paper Mattered

The paper introduces the Transformer, a sequence transduction model (a model that maps one sequence to another, like English → German) that uses only self-attention instead of RNNs (recurrent neural networks) or CNNs (convolutional neural networks).

Before this, translation systems relied on stacked recurrent layers or convolutions to encode and decode sentences. Those systems were accurate but slow to train because they processed tokens mostly in sequence.

The Transformer is fully parallelizable across all positions in a sentence and still learns long-range dependencies (like "the dog … it") with fewer steps. This let it beat previous translation systems in BLEU score (a standard accuracy metric for translation quality) while training in a fraction of the time.