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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 adoi:10.18653/v1/n16-1037 dblp:conf/naacl/JiHE16 fatcat:e3l2es32fjhxpgh2g4qdfse5za