How does attention mechanism improve the performance of sequence-to-sequence models in natural language processing?

Attention mechanism allows sequence-to-sequence models to focus on relevant parts of the input sequence when generating the output sequence. Instead of encoding the entire input sequence into a fixed-length vector, attention mechanisms dynamically weigh input elements based on their relevance to the current decoding step. This enables the model to capture long-range dependencies and improve performance on tasks such as machine translation, text summarization, and question answering.