@InProceedings{stern-fried-klein:2017:EMNLP2017,
  author    = {Stern, Mitchell  and  Fried, Daniel  and  Klein, Dan},
  title     = {Effective Inference for Generative Neural Parsing},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1695--1700},
  abstract  = {Generative neural models have recently achieved state-of-the-art results for
	constituency parsing. However, without a feasible search procedure, their use
	has so far been limited to reranking the output of external parsers in which
	decoding is more tractable. We describe an alternative to the conventional
	action-level beam search used for discriminative neural models that enables us
	to decode directly in these generative models. We then show that by improving
	our basic candidate selection strategy and using a coarse pruning function, we
	can improve accuracy while exploring significantly less of the search space.
	Applied to the model of Choe and Charniak (2016), our inference procedure
	obtains 92.56 F1 on section 23 of the Penn Treebank, surpassing prior
	state-of-the-art results for single-model systems.},
  url       = {https://www.aclweb.org/anthology/D17-1178}
}