  Implicit discourse relation recognition is a crucial component for automatic
discourselevel analysis and nature language understanding. Previous studies
exploit discriminative models that are built on either powerful manual features
or deep discourse representations. In this paper, instead, we explore
generative models and propose a variational neural discourse relation
recognizer. We refer to this model as VarNDRR. VarNDRR establishes a directed
probabilistic model with a latent continuous variable that generates both a
discourse and the relation between the two arguments of the discourse. In order
to perform efficient inference and learning, we introduce neural discourse
relation models to approximate the prior and posterior distributions of the
latent variable, and employ these approximated distributions to optimize a
reparameterized variational lower bound. This allows VarNDRR to be trained with
standard stochastic gradient methods. Experiments on the benchmark data set
show that VarNDRR can achieve comparable results against stateof- the-art
baselines without using any manual features.
