{"title":"基于上下文嵌入的鲁棒仇恨语音检测","authors":"J. Hoffmann, Udo Kruschwitz","doi":"10.4000/BOOKS.AACCADEMIA.6967","DOIUrl":null,"url":null,"abstract":"We describe our approach to addressTask A of the EVALITA 2020 Hate SpeechDetection (HaSpeeDe2) challenge.Wesubmitted two runs that are both based oncontextual embeddings – which we hadchosen due to their effectiveness in solvinga wide range of NLP problems. For ourbaseline run we use stacked embeddingsthat serve as features in a linear SVM. Oursecond run is a simple ensemble approachof three SVMs with majority voting. Bothapproaches outperform the official base-lines by a large margin, and the ensembleclassifier in particular demonstrates robustperformance on different types of test datacoming 6th (out of 27 runs) for news head-lines and 10th (out of 27) for Twitter feeds.","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"602 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"UR NLP @ HaSpeeDe 2 at EVALITA 2020: Towards Robust Hate Speech Detection with Contextual Embeddings\",\"authors\":\"J. Hoffmann, Udo Kruschwitz\",\"doi\":\"10.4000/BOOKS.AACCADEMIA.6967\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe our approach to addressTask A of the EVALITA 2020 Hate SpeechDetection (HaSpeeDe2) challenge.Wesubmitted two runs that are both based oncontextual embeddings – which we hadchosen due to their effectiveness in solvinga wide range of NLP problems. For ourbaseline run we use stacked embeddingsthat serve as features in a linear SVM. Oursecond run is a simple ensemble approachof three SVMs with majority voting. Bothapproaches outperform the official base-lines by a large margin, and the ensembleclassifier in particular demonstrates robustperformance on different types of test datacoming 6th (out of 27 runs) for news head-lines and 10th (out of 27) for Twitter feeds.\",\"PeriodicalId\":184564,\"journal\":{\"name\":\"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020\",\"volume\":\"602 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4000/BOOKS.AACCADEMIA.6967\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4000/BOOKS.AACCADEMIA.6967","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
UR NLP @ HaSpeeDe 2 at EVALITA 2020: Towards Robust Hate Speech Detection with Contextual Embeddings
We describe our approach to addressTask A of the EVALITA 2020 Hate SpeechDetection (HaSpeeDe2) challenge.Wesubmitted two runs that are both based oncontextual embeddings – which we hadchosen due to their effectiveness in solvinga wide range of NLP problems. For ourbaseline run we use stacked embeddingsthat serve as features in a linear SVM. Oursecond run is a simple ensemble approachof three SVMs with majority voting. Bothapproaches outperform the official base-lines by a large margin, and the ensembleclassifier in particular demonstrates robustperformance on different types of test datacoming 6th (out of 27 runs) for news head-lines and 10th (out of 27) for Twitter feeds.