{"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/star-transformer","title":"Star-Transformer","arxiv_id":"1902.09113","date":"2019-02-25","proceeding":"NAACL 2019 6","authors":["Qipeng Guo","Xipeng Qiu","PengFei Liu","Yunfan Shao","xiangyang xue","Zheng Zhang"],"abstract":"Although Transformer has achieved great successes on many NLP tasks, its heavy structure with fully-connected attention connections leads to dependencies on large training data. In this paper, we present Star-Transformer, a lightweight alternative by careful sparsification. 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