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Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)最新文献

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Offensive Language Detection in Nepali Social Media 尼泊尔社交媒体中的攻击性语言检测
Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021) Pub Date : 1900-01-01 DOI: 10.18653/v1/2021.woah-1.7
Nobal B. Niraula, S. Dulal, Diwa Koirala
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引用次数: 7
Context Sensitivity Estimation in Toxicity Detection 毒性检测中的环境敏感性估计
Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021) Pub Date : 1900-01-01 DOI: 10.18653/v1/2021.woah-1.15
A. Xenos, John Pavlopoulos, Ion Androutsopoulos
{"title":"Context Sensitivity Estimation in Toxicity Detection","authors":"A. Xenos, John Pavlopoulos, Ion Androutsopoulos","doi":"10.18653/v1/2021.woah-1.15","DOIUrl":"https://doi.org/10.18653/v1/2021.woah-1.15","url":null,"abstract":"User posts whose perceived toxicity depends on the conversational context are rare in current toxicity detection datasets. Hence, toxicity detectors trained on current datasets will also disregard context, making the detection of context-sensitive toxicity a lot harder when it occurs. We constructed and publicly release a dataset of 10k posts with two kinds of toxicity labels per post, obtained from annotators who considered (i) both the current post and the previous one as context, or (ii) only the current post. We introduce a new task, context-sensitivity estimation, which aims to identify posts whose perceived toxicity changes if the context (previous post) is also considered. Using the new dataset, we show that systems can be developed for this task. Such systems could be used to enhance toxicity detection datasets with more context-dependent posts or to suggest when moderators should consider the parent posts, which may not always be necessary and may introduce additional costs.","PeriodicalId":166161,"journal":{"name":"Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129098347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 11
VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes VL-BERT+:在可恨的多模态模因中检测保护组
Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021) Pub Date : 1900-01-01 DOI: 10.18653/v1/2021.woah-1.22
Piush Aggarwal, Michelle Espranita Liman, Darina Gold, Torsten Zesch
{"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":"https://doi.org/10.18653/v1/2021.woah-1.22","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":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126055897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
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