Self-attention is a mechanism in neural networks where each element of an input sequence evaluates its relationship to all other elements in the same sequence.
This is done by calculating so-called attention scores that indicate how relevant one word is to another.
The scores are used to create weighted combinations of the input elements, thereby better capturing the context of each element.
The mechanism allows models to learn long-range dependencies in sequences without the limitations of recurrent networks.
Self-attention is a central component of modern models like the Transformer, which are leading in areas such as machine translation and text generation.