@InProceedings{cheng-lopez-lapata:2017:Short,
  author    = {Cheng, Jianpeng  and  Lopez, Adam  and  Lapata, Mirella},
  title     = {A Generative Parser with a Discriminative Recognition Algorithm},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {118--124},
  abstract  = {Generative models defining joint distributions over parse trees and sentences
	are useful for parsing and language modeling, but impose restrictions on the
	scope of features and are often outperformed by discriminative models. We
	propose a framework for parsing and language modeling which marries a
	generative model with a discriminative recognition model in an encoder-decoder
	setting. We provide interpretations of the framework based on expectation
	maximization and variational inference, and show that it enables parsing and
	language modeling within a single implementation. On the English Penn
	Treen-bank, our framework obtains competitive performance on constituency
	parsing while matching the state-of-the-art single- model language modeling
	score.},
  url       = {http://aclweb.org/anthology/P17-2019}
}