[D] Backpropagating to LSTM inputs!

Hi, I'm trying an architecture that is a sort of autoencoder, where the encoded representation is a string. In order to deal with differentiability issues, I'm not actually encoding it as a string, but as the softmax of the output of the encoder LSTM. Then, this tensor is fed into the decoder LSTM. However, I am noticing a huge difference (of the order of 10 3 or 10 4) between the grads calculated on the outputs of the decoder LSTM and the inputs during backpropagation. That is, it seems that the LSTM barely propagates back to the input sequence.