{"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/universal-sentence-encoder","title":"Universal Sentence Encoder","arxiv_id":"1803.11175","date":"2018-03-29","proceeding":null,"authors":["Daniel Cer","Yinfei Yang","Sheng-yi Kong","Nan Hua","Nicole Limtiaco","Rhomni St. John","Noah Constant","Mario Guajardo-Cespedes","Steve Yuan","Chris Tar","Yun-Hsuan Sung","Brian Strope","Ray Kurzweil"],"abstract":"We present models for encoding sentences into embedding vectors that\nspecifically target transfer learning to other NLP tasks. 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