We investigate the feasibility of neural syntactic generative models with structured latent variables in which exact inference is tractable.
the sentence is encoded left-to-right by an LStM RNN taking the word embedding of the last predicted word as input at each time step, independent of t. the RNN hidden states h 0:n represent each sentence position in its linear context.