Allowing models to focus on relevant parts of the input sequence.
The attention mechanism was a groundbreaking innovation in deep learning, originally introduced to address the bottleneck of the fixed-size context vector in Seq2Seq models. The core idea behind attention is to allow the decoder to look back at the entire input sequence at each step of the output generation process and decide which parts of the input are most relevant for producing the current output word. Instead of relying on a single context vector that summarizes the whole input, the attention mechanism creates a dynamic, weighted context vector tailored for each output step. Here's how it works: at each decoding step, the decoder's current hidden state is compared with all of the encoder's hidden states (which correspond to each word in the input sequence). This comparison generates a set of 'attention scores,' indicating how well each input word aligns with the current output being generated. These scores are then passed through a softmax function to create a set of 'attention weights,' which are probabilities that sum to one. Finally, a weighted sum of the encoder's hidden states is calculated using these attention weights. This sum becomes the dynamic context vector for the current time step. For example, when translating a sentence from English to French, as the model generates the French word for 'car', the attention mechanism might place a high weight on the English word 'car' in the input sentence. This allows the model to handle long-distance dependencies and significantly improves performance on tasks like machine translation. The concept of 'self-attention,' where a sequence pays attention to itself, became the foundational block of the Transformer architecture, which has since superseded RNN-based models for most NLP tasks.