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Modeling random guessing and task difficulty for truth inference in crowdsourcing
This paper addresses the challenge of truth inference in crowdsourcing applications. We propose a generative method that jointly models tasks' difficulties, workers' abilities and guessing behavior to estimate the truths of crowdsourced tasks, which leads to a more accurate estimation on the workers' abilities and tasks' truths. Experiments demonstrate that the proposed method is more effective for estimating truths of crowdsourced tasks compared with the state-of-art methods.
History
Publication title
AAMAS '19: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent SystemsPagination
2288-2290ISSN
2523-5699Department/School
School of Information and Communication TechnologyPublisher
International Foundation for Autonomous Agents and MultiAgent Systems (IFAAMAS)Place of publication
USAEvent title
AAMAS'19: 18th International Conference on Autonomous Agents and Multiagent SystemsEvent Venue
Montreal, CanadaDate of Event (Start Date)
2019-05-13Date of Event (End Date)
2019-05-17Repository Status
- Restricted