Attention Models

Attention models in natural language processing allow a neural network to focus on specific parts of the input sequence when producing an output. Instead of relying on a fixed-length vector representation of the entire input, these models learn to assign weights to different input elements, indicating their relevance to the current output. This mechanism enables the model to selectively attend to the most important information, improving performance in tasks like machine translation and text summarization.