{"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/paranmt-50m-pushing-the-limits-of","title":"ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations","arxiv_id":"1711.05732","date":"2017-11-15","proceeding":"ACL 2018 7","authors":["John Wieting","Kevin Gimpel"],"abstract":"We describe PARANMT-50M, a dataset of more than 50 million English-English\nsentential paraphrase pairs. We generated the pairs automatically by using\nneural machine translation to translate the non-English side of a large\nparallel corpus, following Wieting et al. (2017). Our hope is that ParaNMT-50M\ncan be a valuable resource for paraphrase generation and can provide a rich\nsource of semantic knowledge to improve downstream natural language\nunderstanding tasks. To show its utility, we use ParaNMT-50M to train\nparaphrastic sentence embeddings that outperform all supervised systems on\nevery SemEval semantic textual similarity competition, in addition to showing\nhow it can be used for paraphrase generation.","url_abs":"http://arxiv.org/abs/1711.05732v2","url_pdf":"http://arxiv.org/pdf/1711.05732v2.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":"natural-language-understanding","task_name":"Natural Language Understanding"},{"task_slug":"paraphrase-generation","task_name":"Paraphrase Generation"},{"task_slug":"semantic-textual-similarity","task_name":"Semantic Textual Similarity"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-embeddings","task_name":"Sentence Embeddings"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[{"slug":"paranmt-50m","name":"PARANMT-50M","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1711.05732","atlas_url":"https://app.syntology.ai/?focus=1711.05732","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}