In sequence-to-sequence models, what is the role of the attention mechanism?

In sequence-to-sequence models, the attention mechanism allows the decoder to focus on different parts of the input sequence when generating each word of the output sequence. Without attention, the decoder relies solely on a fixed-length context vector produced by the encoder, which summarizes the entire input sequence. This can be a bottleneck, especially for long sequences, as all the information needs to be compressed into a single vector. The attention mechanism addresses this limitation by allowing the decoder to selectively attend to different parts of the input sequence at each decoding step. Specifically, for each output word, the attention mechanism calculates a set of weights that indicate the importance of each input word. These weights are then used to create a weighted sum of the encoder's hidden states, which is used as the context vector for that specific output word. For example, in machine translation, when translating a sentence from English to French, the attention mechanism allows the decoder to focus on the relevant English words when generating each French word. If the decoder is generating the French word for 'cat', the attention mechanism would likely assign high weights to the English word 'cat' and lower weights to other words in the sentence. By focusing on the relevant parts of the input sequence, the attention mechanism improves the performance of sequence-to-sequence models, especially for long sequences, and makes them more interpretable.