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Sentence Simplification with Deep Reinforcement Learning

2017-03-31EMNLP 2017Code Available0· sign in to hype

Xingxing Zhang, Mirella Lapata

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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 Dress (as shorthand for Deep REinforcement Sentence Simplification), 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.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
ASSETDress-LSSARI (EASSE>=0.2.1)36.59Unverified
NewselaDRESSSARI27.37Unverified
NewselaDRESS-LSSARI26.63Unverified
PWKP / WikiSmallDRESSBLEU34.53Unverified
PWKP / WikiSmallDRESS-LSBLEU36.32Unverified
TurkCorpusDress-LSSARI (EASSE>=0.2.1)37.27Unverified
TurkCorpusDressSARI (EASSE>=0.2.1)37.08Unverified

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