In the context of sequence transduction models, how does long short-term memory (LSTM) architecture improve the processing of input-output sequences compared to traditional recurrent neural networks (RNNs)?

Traditional RNNs struggle to remember information from far back in a sequence because each hidden state is updated by a simple weighted sum, which quickly loses old signals and makes training hard. LSTM architecture adds a memory cell and three gates—input, forget, and output—that decide when to keep, update, or discard information, letting the model preserve useful signals over long distances. This gating mechanism keeps a stable gradient during training, so the network can learn dependencies that span many time steps without the vanishing‑gradient problem. For example, when translating a long sentence, an LSTM can remember the subject from the first word while still processing later words, whereas a vanilla RNN would forget it. As a result, LSTMs produce more accurate input‑to‑output mappings in tasks like language translation or speech recognition.