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?

Integrating LSTM networks with attention mechanisms in sequence transduction models lets the model remember long‑range dependencies while focusing only on the most relevant parts of the input at each step, which reduces the chance of memorizing noise. The LSTM provides a robust, gated memory that prevents the network from relying on every tiny detail of the training set, while the attention layer selectively weighs useful information, effectively regularizing the learning process. This combination acts like a built‑in dropout: irrelevant or noisy tokens are down‑weighted, so the model cannot overfit to spurious patterns. For example, in translating a long sentence, the attention can ignore filler words that appear only in the training set, forcing the LSTM to learn the core grammatical structure instead of memorizing specific word orders. As a result, the model generalizes better to unseen data, especially on complex datasets.