{"title":"VL-BERT+:在可恨的多模态模因中检测保护组","authors":"Piush Aggarwal, Michelle Espranita Liman, Darina Gold, Torsten Zesch","doi":"10.18653/v1/2021.woah-1.22","DOIUrl":null,"url":null,"abstract":"This paper describes our submission (winning solution for Task A) to the Shared Task on Hateful Meme Detection at WOAH 2021. We build our system on top of a state-of-the-art system for binary hateful meme classification that already uses image tags such as race, gender, and web entities. We add further metadata such as emotions and experiment with data augmentation techniques, as hateful instances are underrepresented in the data set.","PeriodicalId":166161,"journal":{"name":"Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)","volume":"209 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes\",\"authors\":\"Piush Aggarwal, Michelle Espranita Liman, Darina Gold, Torsten Zesch\",\"doi\":\"10.18653/v1/2021.woah-1.22\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes our submission (winning solution for Task A) to the Shared Task on Hateful Meme Detection at WOAH 2021. We build our system on top of a state-of-the-art system for binary hateful meme classification that already uses image tags such as race, gender, and web entities. We add further metadata such as emotions and experiment with data augmentation techniques, as hateful instances are underrepresented in the data set.\",\"PeriodicalId\":166161,\"journal\":{\"name\":\"Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)\",\"volume\":\"209 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18653/v1/2021.woah-1.22\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/2021.woah-1.22","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes
This paper describes our submission (winning solution for Task A) to the Shared Task on Hateful Meme Detection at WOAH 2021. We build our system on top of a state-of-the-art system for binary hateful meme classification that already uses image tags such as race, gender, and web entities. We add further metadata such as emotions and experiment with data augmentation techniques, as hateful instances are underrepresented in the data set.
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