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Modeling random guessing and task difficulty for truth inference in crowdsourcing

Citation

Yang, Y and Bai, Q and Liu, Q, Modeling random guessing and task difficulty for truth inference in crowdsourcing, AAMAS '19: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 13-17 May, 2019, Montreal, Canada, pp. 2288-2290. ISSN 2523-5699 (2019) [Conference Extract]


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DOI: doi:10.5555/3306127.3332087

Abstract

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.

Item Details

Item Type:Conference Extract
Keywords:truth discovery, knowledge discovery, trust, crowdsourcing, truth inference
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Intelligent robotics
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Bai, Q (Dr Quan Bai)
ID Code:138232
Year Published:2019
Deposited By:Information and Communication Technology
Deposited On:2020-03-27
Last Modified:2020-04-25
Downloads:0

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