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)?

LSTM networks keep a special memory cell that can be written to, read from, or forgotten through three gates, so the network can decide when to remember or drop information. This lets the model carry useful context over many steps without the gradients that normally vanish or explode in plain RNNs, making it easier to learn long‑range dependencies in a sequence. Because the gates control how much of the past influences the present, the network can align input and output parts more reliably, which is crucial for tasks like translation or speech recognition. For example, when translating “I love you” into French, an LSTM can remember that “love” should become “aime” even after several intervening words, something a vanilla RNN would struggle to keep track of. Thus, LSTMs improve sequence transduction by preserving relevant information across long sequences and enabling more accurate input‑output mappings.