{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sentence-simplification-with-deep","title":"Sentence Simplification with Deep Reinforcement Learning","arxiv_id":"1703.10931","date":"2017-03-31","proceeding":"EMNLP 2017 9","authors":["Xingxing Zhang","Mirella Lapata"],"abstract":"Sentence simplification aims to make sentences easier to read and understand.\nMost recent approaches draw on insights from machine translation to learn\nsimplification rewrites from monolingual corpora of complex and simple\nsentences. We address the simplification problem with an encoder-decoder model\ncoupled with a deep reinforcement learning framework. Our model, which we call\n{\\sc Dress} (as shorthand for {\\bf D}eep {\\bf RE}inforcement {\\bf S}entence\n{\\bf S}implification), explores the space of possible simplifications while\nlearning to optimize a reward function that encourages outputs which are\nsimple, fluent, and preserve the meaning of the input. Experiments on three\ndatasets demonstrate that our model outperforms competitive simplification\nsystems.","url_abs":"http://arxiv.org/abs/1703.10931v2","url_pdf":"http://arxiv.org/pdf/1703.10931v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sentence-simplification-with-deep","repo_url":"https://github.com/XingxingZhang/dress","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"torch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-compression","task_name":"Sentence Compression"},{"task_slug":"text-simplification","task_name":"Text Simplification"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[{"slug":"wikilarge","name":"WikiLarge","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-simplification-on-asset","task":"Text Simplification","dataset":"ASSET","model":"Dress-LS","rank_in_archive_order":8,"of":12,"metrics":{"BLEU":"86.39*","SARI (EASSE>=0.2.1)":"36.59"},"uses_additional_data":false},{"leaderboard":"/sota/text-simplification-on-newsela","task":"Text Simplification","dataset":"Newsela","model":"DRESS","rank_in_archive_order":8,"of":13,"metrics":{"BLEU":"23.21","SARI":"27.37"},"uses_additional_data":false},{"leaderboard":"/sota/text-simplification-on-newsela","task":"Text Simplification","dataset":"Newsela","model":"DRESS-LS","rank_in_archive_order":10,"of":13,"metrics":{"BLEU":"24.30","SARI":"26.63"},"uses_additional_data":false},{"leaderboard":"/sota/text-simplification-on-pwkp-wikismall","task":"Text Simplification","dataset":"PWKP / WikiSmall","model":"DRESS-LS","rank_in_archive_order":3,"of":11,"metrics":{"BLEU":"36.32","SARI":"27.24"},"uses_additional_data":false},{"leaderboard":"/sota/text-simplification-on-pwkp-wikismall","task":"Text Simplification","dataset":"PWKP / WikiSmall","model":"DRESS","rank_in_archive_order":4,"of":11,"metrics":{"BLEU":"34.53","SARI":"27.48"},"uses_additional_data":false},{"leaderboard":"/sota/text-simplification-on-turkcorpus","task":"Text Simplification","dataset":"TurkCorpus","model":"Dress-LS","rank_in_archive_order":13,"of":25,"metrics":{"BLEU":"80.12","SARI (EASSE>=0.2.1)":"37.27"},"uses_additional_data":false},{"leaderboard":"/sota/text-simplification-on-turkcorpus","task":"Text Simplification","dataset":"TurkCorpus","model":"Dress","rank_in_archive_order":17,"of":25,"metrics":{"BLEU":"77.18","SARI (EASSE>=0.2.1)":"37.08"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.10931","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}