Teacher forcing is a training strategy used in recurrent neural networks (RNNs) where the model receives the actual output from the previous time step as input for the current time step, rather than relying on its own predictions. This approach allows the model to learn more effectively from sequences by reducing error accumulation during training, ultimately leading to better performance in tasks that require sequential memory and accurate predictions over time. It is especially relevant in applications involving sequence-to-sequence models, such as machine translation, where maintaining context and coherence across generated outputs is crucial.
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