Teacher Forcing is an artificial intelligence technique that is used to optimize recurrent neural networks (RNNs). It is also known as “injecting teacher knowledge” because it involves providing additional information to the RNNs so that they can make better predictions. Teacher forcing encourages the model to use the “correct” input in its future predictions, rather than relying on previously predicted outputs.
The name, “Teacher Forcing”, comes from the idea of a teacher enforcing the correct answer to a student and is an extension of the idea of supervised learning in machine learning. Teacher forcing is especially useful when training RNNs since RNNs retain previous states when making future predictions. This is not always beneficial however, as it may lead to fitting to fixed patterns such as performing the same or similar actions in similar contexts.
Teacher forcing is used in the training phase of an RNN to maximize the amount of information that the model can learn. The teacher forcing approach is to set a fixed portion of the training data to be input and the rest to be input from the previous predicted output. This allows the model to receive additional information that it would not normally have access to, which can greatly improve its performance.
Essentially, teacher forcing is an effective way to encourage an RNN to make better predictions by providing additional information during its training. By providing accurate teacher information through teacher forcing during training, it can better learn patterns that will generalize well. This can allow it to better recognize sequences without relying too heavily on previously predicted outputs.
