{"title":"没有陆军，没有海军:BERT半监督学习阿拉伯方言","authors":"Chiyu Zhang, Muhammad Abdul-Mageed","doi":"10.18653/v1/W19-4637","DOIUrl":null,"url":null,"abstract":"We present our deep leaning system submitted to MADAR shared task 2 focused on twitter user dialect identification. We develop tweet-level identification models based on GRUs and BERT in supervised and semi-supervised set-tings. We then introduce a simple, yet effective, method of porting tweet-level labels at the level of users. Our system ranks top 1 in the competition, with 71.70% macro F1 score and 77.40% accuracy.","PeriodicalId":268163,"journal":{"name":"WANLP@ACL 2019","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":"{\"title\":\"No Army, No Navy: BERT Semi-Supervised Learning of Arabic Dialects\",\"authors\":\"Chiyu Zhang, Muhammad Abdul-Mageed\",\"doi\":\"10.18653/v1/W19-4637\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present our deep leaning system submitted to MADAR shared task 2 focused on twitter user dialect identification. We develop tweet-level identification models based on GRUs and BERT in supervised and semi-supervised set-tings. We then introduce a simple, yet effective, method of porting tweet-level labels at the level of users. Our system ranks top 1 in the competition, with 71.70% macro F1 score and 77.40% accuracy.\",\"PeriodicalId\":268163,\"journal\":{\"name\":\"WANLP@ACL 2019\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"30\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"WANLP@ACL 2019\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18653/v1/W19-4637\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"WANLP@ACL 2019","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/W19-4637","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
No Army, No Navy: BERT Semi-Supervised Learning of Arabic Dialects
We present our deep leaning system submitted to MADAR shared task 2 focused on twitter user dialect identification. We develop tweet-level identification models based on GRUs and BERT in supervised and semi-supervised set-tings. We then introduce a simple, yet effective, method of porting tweet-level labels at the level of users. Our system ranks top 1 in the competition, with 71.70% macro F1 score and 77.40% accuracy.
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