  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.
