Second, to capture the dependencies on s <t , we explicitly model the dependencies among local latent variables by inputting z s t−1 to the sentence decoder, so that z s t is conditioned on z s <t and is expected to model smooth transitions in a long text.
we initialize the hidden state h s 0 by encoding the input x, the global planning latent variable z p and the planning result g: where h g T is the last hidden state of GRU g that encodes the planning result g. word-level Generation The word decoder (a GRU) conducts word-level generation; it decodes a sentence s we sample word w t k of s t as follows: we train our model end-to-end.