A Simple Global Neural Discourse Parser

Yichu Zhou, Omri Koshorek, Vivek Srikumar and Jonathan Berant

arXiv preprint arXiv:2009.01312, 2020.

Abstract

Discourse parsing is largely dominated by greedy parsers with manually-designed features, while global parsing is rare due to its computational expense. In this paper, we propose a simple chart-based neural discourse parser that does not require any manually-crafted features and is based on learned span representations only. To overcome the computational challenge, we propose an independence assumption between the label assigned to a node in the tree and the splitting point that separates its children, which results in tractable decoding. We empirically demonstrate that our model achieves the best performance among global parsers, and comparable performance to state-of-art greedy parsers, using only learned span representations.

Bib Entry

@unpublished{zhou2020simple,
  author = {Zhou, Yichu and Koshorek, Omri and Srikumar, Vivek and Berant, Jonathan},
  title = {{A Simple Global Neural Discourse Parser}},
  note = {arXiv preprint arXiv:2009.01312},
  year = {2020}
}

Vivek Srikumar