{"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/multilingual-unsupervised-sentence","title":"MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases","arxiv_id":"2005.00352","date":"2020-05-01","proceeding":"LREC 2022 6","authors":["Louis Martin","Angela Fan","Éric de la Clergerie","Antoine Bordes","Benoît Sagot"],"abstract":"Progress in sentence simplification has been hindered by a lack of labeled parallel simplification data, particularly in languages other than English. We introduce MUSS, a Multilingual Unsupervised Sentence Simplification system that does not require labeled simplification data. MUSS uses a novel approach to sentence simplification that trains strong models using sentence-level paraphrase data instead of proper simplification data. These models leverage unsupervised pretraining and controllable generation mechanisms to flexibly adjust attributes such as length and lexical complexity at inference time. We further present a method to mine such paraphrase data in any language from Common Crawl using semantic sentence embeddings, thus removing the need for labeled data. We evaluate our approach on English, French, and Spanish simplification benchmarks and closely match or outperform the previous best supervised results, despite not using any labeled simplification data. We push the state of the art further by incorporating labeled simplification data.","url_abs":"https://arxiv.org/abs/2005.00352v2","url_pdf":"https://arxiv.org/pdf/2005.00352v2.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":"multilingual-unsupervised-sentence","repo_url":"https://github.com/facebookresearch/muss","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"parallel-corpus-mining","task_name":"Parallel Corpus Mining"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"text-simplification","task_name":"Text Simplification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-simplification-on-asset","task":"Text Simplification","dataset":"ASSET","model":"MUSS (BART+ACCESS Supervised)","rank_in_archive_order":2,"of":12,"metrics":{"BLEU":"72.98","FKGL":"6.05","SARI (EASSE>=0.2.1)":"44.15"},"uses_additional_data":false},{"leaderboard":"/sota/text-simplification-on-asset","task":"Text Simplification","dataset":"ASSET","model":"MUSS (BART+ACCESS Unsupervised)","rank_in_archive_order":5,"of":12,"metrics":{"FKGL":"8.23","SARI (EASSE>=0.2.1)":"42.65"},"uses_additional_data":false},{"leaderboard":"/sota/text-simplification-on-turkcorpus","task":"Text Simplification","dataset":"TurkCorpus","model":"MUSS (BART+ACCESS Supervised)","rank_in_archive_order":2,"of":25,"metrics":{"BLEU":"78.17","FKGL":"7.60","SARI (EASSE>=0.2.1)":"42.53"},"uses_additional_data":false},{"leaderboard":"/sota/text-simplification-on-turkcorpus","task":"Text Simplification","dataset":"TurkCorpus","model":"MUSS (BART+ACCESS Unsupervised)","rank_in_archive_order":6,"of":25,"metrics":{"FKGL":"8.79","SARI (EASSE>=0.2.1)":"40.85"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2005.00352","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}