Which of the following statements best describes the function of sequence transduction models in natural language processing?

Sequence transduction models take a string of words, numbers, or characters as input and produce another string that can be longer, shorter, or of the same length, while keeping the meaning or structure you need. They learn this mapping by looking at many paired examples, such as English sentences and their French translations, so they can later generate the correct output for new inputs. The key idea is that the model handles variable‑length inputs and outputs, unlike simple classifiers that only assign a single label. For instance, when you give it the English sentence “I love pizza,” a transduction model can output the French “J’aime la pizza,” changing the language but keeping the sense. This ability makes them essential for tasks like translation, summarization, and speech recognition.