Backpropagation through structure is a gradient-based training technique for certain structured or recurrent neural network architectures. It extends the idea of propagating error through network components whose structure may reflect recursive or relational data.
A gradient-based technique for training recurrent neural networks, proposed in a 1996 paper written by Christoph Goller and Andreas KΓΌchler.