{"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/learning-how-to-simplify-from-explicit","title":"Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs","arxiv_id":null,"date":"2017-11-01","proceeding":"IJCNLP 2017 11","authors":["Fern Alva-Manchego","o","Joachim Bingel","Gustavo Paetzold","Carolina Scarton","Lucia Specia"],"abstract":"Current research in text simplification has been hampered by two central problems: (i) the small amount of high-quality parallel simplification data available, and (ii) the lack of explicit annotations of simplification operations, such as deletions or substitutions, on existing data. While the recently introduced Newsela corpus has alleviated the first problem, simplifications still need to be learned directly from parallel text using black-box, end-to-end approaches rather than from explicit annotations. These complex-simple parallel sentence pairs often differ to such a high degree that generalization becomes difficult. End-to-end models also make it hard to interpret what is actually learned from data. We propose a method that decomposes the task of TS into its sub-problems. We devise a way to automatically identify operations in a parallel corpus and introduce a sequence-labeling approach based on these annotations. Finally, we provide insights on the types of transformations that different approaches can model.","url_abs":"https://aclanthology.org/I17-1030","url_pdf":"https://aclanthology.org/I17-1030.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":"learning-how-to-simplify-from-explicit","repo_url":"https://github.com/ghpaetzold/massalign","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-compression","task_name":"Sentence Compression"},{"task_slug":"text-simplification","task_name":"Text Simplification"}],"methods":[{"method_slug":"ts","method_name":"TS"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-simplification-on-newsela","task":"Text Simplification","dataset":"Newsela","model":"SeqLabel","rank_in_archive_order":11,"of":13,"metrics":{"SARI":"29.53*"},"uses_additional_data":false},{"leaderboard":"/sota/text-simplification-on-pwkp-wikismall","task":"Text Simplification","dataset":"PWKP / WikiSmall","model":"SeqLabel","rank_in_archive_order":9,"of":11,"metrics":{"SARI":"30.50*"},"uses_additional_data":false},{"leaderboard":"/sota/text-simplification-on-turkcorpus","task":"Text Simplification","dataset":"TurkCorpus","model":"SeqLabel","rank_in_archive_order":25,"of":25,"metrics":{"SARI (EASSE>=0.2.1)":"37.08*"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}