A Latent Variable Recurrent Neural Network for Discourse Relation Language Models

Understand

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.