Representation Learning for Text-level Discourse Parsing

  title={Representation Learning for Text-level Discourse Parsing},
  author={Yangfeng Ji and Jacob Eisenstein},
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… CONTINUE READING
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