What is the primary reason that sequence transduction models have improved performance in translating input sequences into output sequences?

The main reason sequence transduction models perform better is that they use an attention mechanism, which lets the model focus on the most relevant parts of the input when generating each output word. This replaces the fixed-size hidden state of older recurrent models, so the model can remember and use information from far back in the sequence. Because attention is computed in parallel, training is faster and more efficient, and the model learns stronger long‑range dependencies. For example, when translating “The cat sat on the mat,” the model can directly align “cat” with “gato” in Spanish, even though the words are several positions apart. This ability to capture long‑distance relationships and train efficiently gives sequence transduction models a clear performance edge.