Supposing we have a smaller vocabulary, the document represented by this vocabulary is y = {y i } m i=1 , where m < n is the number of words and y i ∈ R e is the word embedding of the word at position i.
but it is not good at modeling semantics (Dieng et al., 2017) and may cause the latent variable collapse problem (bowman et al., 2016) which leads that the decoder ignores information from the inferred latent codes.