What is the primary function of sequence transduction models in neural networks?

Sequence transduction models in neural networks take a series of inputs—like words, sounds, or pixels—and produce a new series that represents the same information in a different form. They learn to map each element of the input sequence to an appropriate element of the output sequence, handling variable lengths and complex dependencies. For instance, a translation model reads the English sentence “I love you” and outputs the Spanish sentence “Te amo”, aligning words while preserving meaning. By training on many paired examples, the model learns the statistical patterns that guide this conversion, enabling tasks such as speech recognition, machine translation, and time‑series forecasting. The core idea is to transform one sequence into another that best reflects the intended relationship.