What is the attention mechanism

The attention mechanism is a component in neural networks that enables models to dynamically focus on the most relevant parts of input data when making predictions or generating outputs. It assigns varying weights or importance scores to different elements within the input sequence.

It works by computing compatibility scores between a target element (like a decoder state) and all source elements (like encoder states). These scores are normalized, typically using a softmax function, to produce attention weights reflecting the relative importance of each source element. A weighted sum (context vector) of the source elements, using these weights, is then created and used by the model. This allows the model to selectively concentrate on pertinent information based on the current processing state, overcoming limitations of fixed-length vector representations. It is fundamentally applicable to sequence-to-sequence tasks and forms the basis of self-attention in Transformers.

The attention mechanism has revolutionized neural machine translation (NMT) and become foundational across natural language processing (NLP). By allowing models to access all relevant parts of the input sequence flexibly rather than relying on a single bottleneck vector, it significantly improves handling of long sequences and complex dependencies. Key applications beyond translation include text summarization, question answering, and image captioning, providing models with the vital capability to effectively 'pay attention' to the most salient information for the task.