Sentence Simplification with Deep Reinforcement Learning

Xingxing Zhang, Mirella Lapata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationProceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Place of PublicationCopenhagen, Denmark
PublisherAssociation for Computational Linguistics
Pages584-594
Number of pages11
ISBN (Print)978-1-945626-83-8
DOIs
Publication statusPublished - 11 Sep 2017
EventEMNLP 2017: Conference on Empirical Methods in Natural Language Processing - Copenhagen, Denmark
Duration: 7 Sep 201711 Sep 2017
http://emnlp2017.net/index.html
http://emnlp2017.net/

Conference

ConferenceEMNLP 2017: Conference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2017
Country/TerritoryDenmark
CityCopenhagen
Period7/09/1711/09/17
Internet address

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