In the context of Sequence Transduction Models, how can the integration of Long Short-Term Memory (LSTM) networks and attention mechanisms help mitigate the issue of overfitting during training on complex datasets?

In sequence transduction, LSTMs capture long‑range dependencies while attention lets the model focus on relevant parts of the input, reducing reliance on memorizing noise. By learning to weight only the useful tokens, attention lowers the effective capacity of the network, which discourages over‑fitting to idiosyncratic patterns in a complex dataset. The LSTM’s gating mechanism further controls gradient flow, preventing the network from fitting every minor fluctuation in the training data. Together, they act like a regularizer: the attention mask shrinks the parameter space that matters, and the LSTM gates keep the representation smooth, so the model generalizes better. For example, when translating a sentence, the attention can ignore rare, noisy words while the LSTM keeps track of the overall sentence structure, leading to fewer spurious learned patterns.