{"title":"使用透明解决方案和语言专业知识的自动内容审核","authors":"Veronika Solopova","doi":"10.24963/ijcai.2023/823","DOIUrl":null,"url":null,"abstract":"Since the dawn of Transformer-based models, the trade-off between transparency and accuracy has been a topical issue in the NLP community. Working towards ethical and transparent automated content moderation (ACM), my goal is to find where it is still relevant to implement linguistic expertise. I show that transparent statistical models based on linguistic knowledge can still be competitive, while linguistic features have many other useful applications.","PeriodicalId":394530,"journal":{"name":"International Joint Conference on Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automated Content Moderation Using Transparent Solutions and Linguistic Expertise\",\"authors\":\"Veronika Solopova\",\"doi\":\"10.24963/ijcai.2023/823\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Since the dawn of Transformer-based models, the trade-off between transparency and accuracy has been a topical issue in the NLP community. Working towards ethical and transparent automated content moderation (ACM), my goal is to find where it is still relevant to implement linguistic expertise. I show that transparent statistical models based on linguistic knowledge can still be competitive, while linguistic features have many other useful applications.\",\"PeriodicalId\":394530,\"journal\":{\"name\":\"International Joint Conference on Artificial Intelligence\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Joint Conference on Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.24963/ijcai.2023/823\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Joint Conference on Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24963/ijcai.2023/823","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automated Content Moderation Using Transparent Solutions and Linguistic Expertise
Since the dawn of Transformer-based models, the trade-off between transparency and accuracy has been a topical issue in the NLP community. Working towards ethical and transparent automated content moderation (ACM), my goal is to find where it is still relevant to implement linguistic expertise. I show that transparent statistical models based on linguistic knowledge can still be competitive, while linguistic features have many other useful applications.