Each latent topic z is described by a multinomial distribution over words P(w i |z), and each word in a document is attributed to a particular latent topic (Blei et al., 2003).
the adaptation vector f w represents a vector of posterior probabilities over latent topics for word sequence w: LDA was found to perform better for RNNLM adaptation than other representations in (Mikolov and Zweig, 2012;Chen et al., 2015).