Paper: Representation Learning for Text-level Discourse Parsing

ACL ID P14-1002
Title Representation Learning for Text-level Discourse Parsing
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014

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.