Self-Attention

Self-attention is a mechanism in neural networks that allows the model to weigh the significance of different words in a sequence relative to each other. It computes a set of attention scores for each word, indicating how much focus to place on other words when encoding a particular word. This approach enhances the model's ability to capture contextual relationships, making it especially effective in tasks such as machine translation and text summarization. Self-attention is a core component of transformer architectures, which have revolutionized natural language processing by enabling parallel processing of data and improving efficiency.