  This paper presents a novel latent variable recurrent neural network
architecture for jointly modeling sequences of words and (possibly latent)
discourse relations between adjacent sentences. A recurrent neural network
generates individual words, thus reaping the benefits of
discriminatively-trained vector representations. The discourse relations are
represented with a latent variable, which can be predicted or marginalized,
depending on the task. The resulting model can therefore employ a training
objective that includes not only discourse relation classification, but also
word prediction. As a result, it outperforms state-of-the-art alternatives for
two tasks: implicit discourse relation classification in the Penn Discourse
Treebank, and dialog act classification in the Switchboard corpus. Furthermore,
by marginalizing over latent discourse relations at test time, we obtain a
discourse informed language model, which improves over a strong LSTM baseline.
