The attention mechanism solves the core limitation of traditional encoder–decoder models by dynamically focusing on relevant input tokens at each decoding step. This article explains why attention is needed, how alignment scores and context vectors work, and why attention dramatically improves translation quality for long sequences.

Sequence-to-sequence models form the foundation of modern neural machine translation. In this article, I explain the encoder–decoder architecture from first principles, covering variable-length sequences, training with teacher forcing, backpropagation through time, prediction flow, and key improvements such as embeddings and deep LSTMs—using intuitive explanations and clear diagrams.