{"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/ernie-enhanced-representation-through","title":"ERNIE: Enhanced Representation through Knowledge Integration","arxiv_id":"1904.09223","date":"2019-04-19","proceeding":null,"authors":["Yu Sun","Shuohuan Wang","Yukun Li","Shikun Feng","Xuyi Chen","Han Zhang","Xin Tian","Danxiang Zhu","Hao Tian","Hua Wu"],"abstract":"We present a novel language representation model enhanced by knowledge called\nERNIE (Enhanced Representation through kNowledge IntEgration). Inspired by the\nmasking strategy of BERT, ERNIE is designed to learn language representation\nenhanced by knowledge masking strategies, which includes entity-level masking\nand phrase-level masking. Entity-level strategy masks entities which are\nusually composed of multiple words.Phrase-level strategy masks the whole phrase\nwhich is composed of several words standing together as a conceptual\nunit.Experimental results show that ERNIE outperforms other baseline methods,\nachieving new state-of-the-art results on five Chinese natural language\nprocessing tasks including natural language inference, semantic similarity,\nnamed entity recognition, sentiment analysis and question answering. We also\ndemonstrate that ERNIE has more powerful knowledge inference capacity on a\ncloze test.","url_abs":"http://arxiv.org/abs/1904.09223v1","url_pdf":"http://arxiv.org/pdf/1904.09223v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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