{"title":"基于有序神经元LSTM的XLM-RoBERTa分类任务在EVALITA上的应用[j]","authors":"Xiaozhi Ou, Hongling Li","doi":"10.4000/BOOKS.AACCADEMIA.6912","DOIUrl":null,"url":null,"abstract":"English. This paper describes the system that team YNU OXZ submitted for EVALITA 2020. We participate in the shared task on Automatic Misogyny Identification (AMI) and Hate Speech Detection (HaSpeeDe 2) at the 7th evaluation campaign EVALITA 2020. For HaSpeeDe 2, we participate in Task A Hate Speech Detection and submitted two-run results for the news headline test and tweets headline test, respectively. Our submitted run is based on the pre-trained multilanguage model XLM-RoBERTa, and input into Convolution Neural Network and K-max Pooling (CNN + K-max Pooling). Then, an Ordered Neurons LSTM (ONLSTM) is added to the previous representation and submitted to a linear decision function. Regarding the AMI shared task for the automatic identification of misogynous content in the Italian language. We participate in subtask A about Misogyny & Aggressive Behaviour Identification. Our system is similar to the one defined for HaSpeeDe and is based on the pre-trained multi-language model XLMRoBERTa, an Ordered Neurons LSTM (ON-LSTM), a Capsule Network, and a final classifier.","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"YNU_OXZ @ HaSpeeDe 2 and AMI : XLM-RoBERTa with Ordered Neurons LSTM for Classification Task at EVALITA 2020\",\"authors\":\"Xiaozhi Ou, Hongling Li\",\"doi\":\"10.4000/BOOKS.AACCADEMIA.6912\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"English. This paper describes the system that team YNU OXZ submitted for EVALITA 2020. We participate in the shared task on Automatic Misogyny Identification (AMI) and Hate Speech Detection (HaSpeeDe 2) at the 7th evaluation campaign EVALITA 2020. For HaSpeeDe 2, we participate in Task A Hate Speech Detection and submitted two-run results for the news headline test and tweets headline test, respectively. Our submitted run is based on the pre-trained multilanguage model XLM-RoBERTa, and input into Convolution Neural Network and K-max Pooling (CNN + K-max Pooling). Then, an Ordered Neurons LSTM (ONLSTM) is added to the previous representation and submitted to a linear decision function. Regarding the AMI shared task for the automatic identification of misogynous content in the Italian language. We participate in subtask A about Misogyny & Aggressive Behaviour Identification. Our system is similar to the one defined for HaSpeeDe and is based on the pre-trained multi-language model XLMRoBERTa, an Ordered Neurons LSTM (ON-LSTM), a Capsule Network, and a final classifier.\",\"PeriodicalId\":184564,\"journal\":{\"name\":\"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"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.6912\",\"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.6912","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
摘要
英语。本文介绍了YNU OXZ团队为EVALITA 2020提交的系统。在第七届EVALITA 2020评估活动中,我们参与了关于厌女症自动识别(AMI)和仇恨言论检测(HaSpeeDe 2)的共享任务。对于HaSpeeDe 2,我们参与了Task A Hate Speech Detection,并分别提交了news标题测试和tweets标题测试的两轮结果。我们提交的运行是基于预训练的多语言模型XLM-RoBERTa,并输入卷积神经网络和K-max Pooling (CNN + K-max Pooling)。然后,将有序神经元LSTM (ONLSTM)添加到之前的表示中,并提交给线性决策函数。关于AMI共享的意大利语中厌女内容的自动识别任务。我们参与关于厌女症和攻击行为识别的子任务A。我们的系统类似于为HaSpeeDe定义的系统,它基于预训练的多语言模型xlroberta,一个有序神经元LSTM (on -LSTM),一个胶囊网络和一个最终分类器。
YNU_OXZ @ HaSpeeDe 2 and AMI : XLM-RoBERTa with Ordered Neurons LSTM for Classification Task at EVALITA 2020
English. This paper describes the system that team YNU OXZ submitted for EVALITA 2020. We participate in the shared task on Automatic Misogyny Identification (AMI) and Hate Speech Detection (HaSpeeDe 2) at the 7th evaluation campaign EVALITA 2020. For HaSpeeDe 2, we participate in Task A Hate Speech Detection and submitted two-run results for the news headline test and tweets headline test, respectively. Our submitted run is based on the pre-trained multilanguage model XLM-RoBERTa, and input into Convolution Neural Network and K-max Pooling (CNN + K-max Pooling). Then, an Ordered Neurons LSTM (ONLSTM) is added to the previous representation and submitted to a linear decision function. Regarding the AMI shared task for the automatic identification of misogynous content in the Italian language. We participate in subtask A about Misogyny & Aggressive Behaviour Identification. Our system is similar to the one defined for HaSpeeDe and is based on the pre-trained multi-language model XLMRoBERTa, an Ordered Neurons LSTM (ON-LSTM), a Capsule Network, and a final classifier.