著者
芦川 将之 川村 隆浩 大須賀 昭彦
出版者
一般社団法人 人工知能学会
雑誌
人工知能学会論文誌 (ISSN:13460714)
巻号頁・発行日
vol.32, no.3, pp.B-G81_1-13, 2017-05-01 (Released:2017-05-01)
参考文献数
31

Current crowdsourcing platforms such as Amazon Mechanical Turk provide an attractive solution Crowdsourcing platforms provide an attractive solution for processing numerous tasks at a low cost. However, insufficient quality control remains a major concern. Therefore, we developed a private crowdsourcing system that allows us to devise quality control methods. In the present study, we propose a grade-based training method for workers in order to avoid simple exclusion of low-quality workers and shrinkage of the crowdsourcing market in the near future. Our training method utilizes probabilistic networks to estimate correlations between tasks based on workers’ records for 18.5 million tasks and then allocates pre-learning tasks to the workers to raise the accuracy of target tasks according to the task correlations. In an experiment, the method automatically allocated 31 pre-learning task categories for 9 target task categories, and after the training of the pre-learning tasks, we confirmed that the accuracy of the target tasks was raised by 7.8 points on average. This result was comparatively higher than those of pre-learning tasks allocated using other methods, such as decision trees. We thus confirmed that the task correlations can be estimated using a large amount of worker records, and that these are useful for the grade-based training of low-quality workers.

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途中で読むのやめたww クラウドソーシングワーカーの段階的育成方法の提案 https://t.co/3FzEYZ0zM5
クラウドソーシングワーカーの育成方法を、大学の先生方が論文にしておる。ベイズの定理とか使って論じてる。学者さんは小難しく考えるのが好きだなあ。 https://t.co/Xrk2E2oOmh

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