Sentence Simplification with Deep Reinforcement Learning
This work addresses the problem of making text more accessible for readers with lower literacy or language proficiency, representing an incremental improvement over existing methods.
The paper tackled sentence simplification by developing a deep reinforcement learning model called Dress, which optimizes for simplicity, fluency, and meaning preservation, and it outperformed competitive systems on three datasets.
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.