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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.
|Title of host publication||Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing|
|Place of Publication||Copenhagen, Denmark|
|Publisher||Association for Computational Linguistics|
|Number of pages||11|
|Publication status||Published - 11 Sep 2017|
|Event||EMNLP 2017: Conference on Empirical Methods in Natural Language Processing - Copenhagen, Denmark|
Duration: 7 Sep 2017 → 11 Sep 2017
|Conference||EMNLP 2017: Conference on Empirical Methods in Natural Language Processing|
|Abbreviated title||EMNLP 2017|
|Period||7/09/17 → 11/09/17|
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1/09/16 → 28/02/22