Maximilien Servajean, A. Joly, D. Shasha, Julien Champ, Esther Pacitti
{"title":"ThePlantGame:积极训练特定领域众包的人类注释者","authors":"Maximilien Servajean, A. Joly, D. Shasha, Julien Champ, Esther Pacitti","doi":"10.1145/2964284.2973820","DOIUrl":null,"url":null,"abstract":"In a typical citizen science/crowdsourcing environment, the contributors label items. When there are few labels, it is straightforward to train contributors and judge the quality of their labels by giving a few examples with known answers. Neither is true when there are thousands of domain-specific labels and annotators with heterogeneous skills. This demo paper presents an Active User Training framework implemented as a serious game called ThePlantGame. It is based on a set of data-driven algorithms allowing to (i) actively train annotators, and (ii) evaluate the quality of contributors' answers on new test items to optimize predictions.","PeriodicalId":140670,"journal":{"name":"Proceedings of the 24th ACM international conference on Multimedia","volume":"128 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"ThePlantGame: Actively Training Human Annotators for Domain-specific Crowdsourcing\",\"authors\":\"Maximilien Servajean, A. Joly, D. Shasha, Julien Champ, Esther Pacitti\",\"doi\":\"10.1145/2964284.2973820\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In a typical citizen science/crowdsourcing environment, the contributors label items. When there are few labels, it is straightforward to train contributors and judge the quality of their labels by giving a few examples with known answers. Neither is true when there are thousands of domain-specific labels and annotators with heterogeneous skills. This demo paper presents an Active User Training framework implemented as a serious game called ThePlantGame. It is based on a set of data-driven algorithms allowing to (i) actively train annotators, and (ii) evaluate the quality of contributors' answers on new test items to optimize predictions.\",\"PeriodicalId\":140670,\"journal\":{\"name\":\"Proceedings of the 24th ACM international conference on Multimedia\",\"volume\":\"128 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 24th ACM international conference on Multimedia\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2964284.2973820\",\"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 24th ACM international conference on Multimedia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2964284.2973820","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ThePlantGame: Actively Training Human Annotators for Domain-specific Crowdsourcing
In a typical citizen science/crowdsourcing environment, the contributors label items. When there are few labels, it is straightforward to train contributors and judge the quality of their labels by giving a few examples with known answers. Neither is true when there are thousands of domain-specific labels and annotators with heterogeneous skills. This demo paper presents an Active User Training framework implemented as a serious game called ThePlantGame. It is based on a set of data-driven algorithms allowing to (i) actively train annotators, and (ii) evaluate the quality of contributors' answers on new test items to optimize predictions.
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