@InProceedings{zhang-lapata:2017:EMNLP2017,
  author    = {Zhang, Xingxing  and  Lapata, Mirella},
  title     = {Sentence Simplification with Deep Reinforcement Learning},
  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     = {584--594},
  abstract  = {Sentence simplification aims to make sentences easier to read and
	  understand. Most recent approaches draw on insights from machine
	  translation to learn simplification rewrites from monolingual
	  corpora of complex and simple sentences. We address the
	  simplification problem with an encoder-decoder model coupled with a
	  deep reinforcement learning framework. Our model, which we call {\sc
	    Dress} (as shorthand for {\bf D}eep {\bf RE}inforcement {\bf
	    S}entence {\bf S}implification), explores the space of possible
	  simplifications while learning to optimize a reward function that
	  encourages outputs which are simple, fluent, and preserve the
	  meaning of the input. Experiments on three datasets demonstrate that
	  our model outperforms competitive simplification
	  systems.},
  url       = {https://www.aclweb.org/anthology/D17-1062}
}