How Attention Bridges Encoder and Decoder in Neural Translation

Attention mechanisms revolutionize sequence-to-sequence models by overcoming the information bottleneck of fixed-size context vectors. Instead of compressing all encoder information into a single vector, attention enables decoders to dynamically select relevant source information at each decoding step, dramatically improving machine translation and sequential transformation tasks.