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 while 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 earlier in the sequence. Because attention is computed in parallel, training is faster and more efficient, and the model learns clearer alignments between source and target tokens. For example, when translating “I love you” into Spanish, the model can directly attend to “love” when producing “te quiero,” rather than relying on a compressed memory that might miss that word. This combination of focused context and efficient computation is why modern transduction models excel.