I'm reading Sequence to Sequence Learning with Neural Networks and there's a thing that I couldn't quite grasp.
Paper says the encoder outputs a vector to be fed to the decoder. More precisely
Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector
However, when I look at the diagram:
there's no such vector here. What I understand from this diagram is decoder RNN takes the weights of the last encoder cell as an input.
Which one is correct? can you explain?
Stanford notes put it as
The final hidden state of the cell will then become C
So, is there no vector?