{"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-memory-augmented","title":"Sentence Simplification with Memory-Augmented Neural Networks","arxiv_id":"1804.07445","date":"2018-04-20","proceeding":"NAACL 2018 6","authors":["Tu Vu","Baotian Hu","Tsendsuren Munkhdalai","Hong Yu"],"abstract":"Sentence simplification aims to simplify the content and structure of complex\nsentences, and thus make them easier to interpret for human readers, and easier\nto process for downstream NLP applications. Recent advances in neural machine\ntranslation have paved the way for novel approaches to the task. In this paper,\nwe adapt an architecture with augmented memory capacities called Neural\nSemantic Encoders (Munkhdalai and Yu, 2017) for sentence simplification. Our\nexperiments demonstrate the effectiveness of our approach on different\nsimplification datasets, both in terms of automatic evaluation measures and\nhuman judgments.","url_abs":"http://arxiv.org/abs/1804.07445v1","url_pdf":"http://arxiv.org/pdf/1804.07445v1.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":[],"tasks":[{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"text-simplification","task_name":"Text Simplification"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-simplification-on-newsela","task":"Text Simplification","dataset":"Newsela","model":"NSELSTM-S","rank_in_archive_order":6,"of":13,"metrics":{"BLEU":"22.62","SARI":"29.58"},"uses_additional_data":false},{"leaderboard":"/sota/text-simplification-on-newsela","task":"Text Simplification","dataset":"Newsela","model":"NSELSTM-B","rank_in_archive_order":7,"of":13,"metrics":{"BLEU":"26.31","SARI":"27.42"},"uses_additional_data":false},{"leaderboard":"/sota/text-simplification-on-pwkp-wikismall","task":"Text Simplification","dataset":"PWKP / WikiSmall","model":"NSELSTM-B","rank_in_archive_order":1,"of":11,"metrics":{"BLEU":"53.42","SARI":"17.47"},"uses_additional_data":false},{"leaderboard":"/sota/text-simplification-on-pwkp-wikismall","task":"Text Simplification","dataset":"PWKP / WikiSmall","model":"NSELSTM-S","rank_in_archive_order":5,"of":11,"metrics":{"BLEU":"29.72","SARI":"29.75"},"uses_additional_data":false},{"leaderboard":"/sota/text-simplification-on-turkcorpus","task":"Text Simplification","dataset":"TurkCorpus","model":"NSELSTM-S","rank_in_archive_order":18,"of":25,"metrics":{"BLEU":"80.43","SARI (EASSE>=0.2.1)":"36.88"},"uses_additional_data":false},{"leaderboard":"/sota/text-simplification-on-turkcorpus","task":"Text Simplification","dataset":"TurkCorpus","model":"NSELSTM-B","rank_in_archive_order":21,"of":25,"metrics":{"BLEU":"92.02","SARI (EASSE>=0.2.1)":"33.43"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.07445","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}