Variational Neural Discourse Relation Recognizer
This work addresses the problem of discourse-level analysis for natural language understanding, presenting an incremental approach by applying variational methods to a known task.
The paper tackles implicit discourse relation recognition by proposing a variational neural discourse relation recognizer (VarNDRR), a generative model that uses a latent variable to generate discourse and relations, achieving comparable results to state-of-the-art baselines without manual features on benchmark datasets.
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.