Representation Learning for Text level Discourse Parsing
Text level discourse parsing is notoriously difficult, as distinctions between discourse relations require subtle semantic judgments that are not easily captured using standard features. In this paper, we present a representation learning approach, in which we transform surface features into a latent space that facilitates RST discourse parsing. By combining the machinery of large margin transition based structured prediction with representation learning, our method jointly learns to parse discourse while at the same time learning a discourse driven projection of surface features. The resulting shift reduce discourse parser obtains substantial improvements over the previous state of the art in predicting relations and nuclearity on the RST Treebank.
ACL