In the context of sequence transduction models, which component is crucial for effectively capturing long-range dependencies in sequences?

In sequence transduction models, the attention mechanism—especially self‑attention in Transformer architectures—is the key component that lets the model look at any part of the input when generating each output token. By computing weighted sums over all positions, attention bypasses the fixed‑size context windows of recurrent or convolutional layers, so distant tokens can influence each other directly. This direct connection eliminates the need to propagate information through many intermediate steps, which is why it handles long‑range dependencies efficiently. For instance, when translating a long sentence, attention can immediately align a subject word at the beginning with a verb at the end, something a vanilla RNN would struggle to remember.