Attention Accelerates Training Without Improving Accuracy
Adding an attention mechanism to LSTM models reduced training time by nearly an order of magnitude, from 7-10 epochs to 2-3 epochs before diminishing returns, without significantly improving final accuracy (roughly 1% ROC AUC gain).
The mechanism: attention allows the model to look at all hidden states in a sequence rather than just the final one. Crucial words get picked up faster because the model can weight their contribution directly instead of relying on information to propagate through the sequence.
This reframes what attention “buys you.” It’s not just about better representations. It’s about faster learning, better gradient descent vectors that converge more quickly.
For production systems, this has real implications. If training time is a constraint (frequent retraining, large datasets, limited compute), attention may be worthwhile even if final accuracy is similar.