Teacher Forcing

Teacher forcing is a training strategy used primarily in sequence-to-sequence models within the field of machine learning, particularly in natural language processing. In this approach, the model is trained on the actual output from the training dataset rather than its own predictions during the training phase. This technique helps the model to learn the correct sequence of outputs more effectively, as it reduces the accumulation of errors that can occur when a model generates its own predictions. Teacher forcing is commonly used in tasks such as language translation, text generation, and speech recognition, where maintaining the correct context and sequence is crucial for performance.