Representation Learning for Text-level Discourse Parsing 论文

摘要

Text-level discourse parsing is notoriously difficult, as distinctions between discourse relations require subtle semantic judg-ments 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 dis-course parsing. By combining the machin-ery of large-margin transition-based struc-tured prediction with representation learn-ing, our method jointly learns to parse dis-course while at the same time learning a discourse-driven projection of surface fea-tures. The resulting shift-reduce discourse parser obtains substantial improvements over the previous state-of-the-art in pre-dicting relations and nuclearity on the RST Treebank. 1