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

Sequence transduction models in neural networks are designed to take an input sequence and produce a different output sequence, often of a different length or format. They learn the mapping between the two sequences by training on many paired examples, so they can predict the correct output for new inputs. The core idea is that the model captures how each element of the output depends on the whole input, using attention or recurrent mechanisms. For example, a model that translates English sentences into French learns to output a French sentence that corresponds to the meaning of the English input. This ability to convert one sequence into another is the primary function of these models.