By visualizing the type of conversations found by the two models, they show that the output of the HMM+Topic model is more interpretable than that of the HMM one; however, their classification accuracy is not empirically evaluated.
ji, Haffari, and Eisenstein (2016) propose a latent variable RNN that can jointly model sequences of words (i.e., language modeling) and discourse relations between adjacent sentences.