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—is essential for capturing long‑range dependencies. Attention lets the model look directly at any other part of the sequence when computing a representation for a given token, rather than relying only on local context. This means a word can “see” far‑away words in the same sentence or document, which a simple recurrent step would miss or attenuate over many layers. For example, in translating a sentence, the attention on the word “government” can help correctly translate a later word “policy” that appears many tokens away. Thus, attention provides the global view needed for accurate long‑range modeling.