Definition
A training procedure for sequence models where the model is fed the ground-truth previous token as input at the current step, rather than its own prediction, to stabilize and speed up training.
In short: A training procedure for sequence models where the model is fed the ground-truth previous token as input at the current step, rather than its own prediction, to stabilize and speed up training.