@InProceedings{stern-andreas-klein:2017:Long,
  author    = {Stern, Mitchell  and  Andreas, Jacob  and  Klein, Dan},
  title     = {A Minimal Span-Based Neural Constituency Parser},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {818--827},
  abstract  = {In this work, we present a minimal neural model for constituency parsing based
	on independent scoring of labels and spans. We show that this model is not only
	compatible with classical dynamic programming techniques, but also admits a
	novel greedy top-down inference algorithm based on recursive partitioning of
	the input. We demonstrate empirically that both prediction schemes are
	competitive with recent work, and when combined with basic extensions to the
	scoring model are capable of achieving state-of-the-art single-model
	performance on the Penn Treebank (91.79 F1) and strong performance on the
	French Treebank (82.23 F1).},
  url       = {http://aclweb.org/anthology/P17-1076}
}