However, our work differs from theirs significantly, which can be summarized in the following three aspects: 1) they employ the recurrent neural network to represent the discourse arguments, while we use the simple feedforward neural network; 2) they treat the discourse relations directly as latent variables, rather than the underlying semantic representation of discourses; 3) their model is optimized in terms of the data likelihood, since the discourse relations are observed during training.
varNDRR is optimized under the variational theory.