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Modeling random guessing and task difficulty for truth inference in crowdsourcing

conference contribution
posted on 2023-05-24, 19:48 authored by Yang, Y, Quan BaiQuan Bai, Liu, Q
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 Systems

Pagination

2288-2290

ISSN

2523-5699

Department/School

School of Information and Communication Technology

Publisher

International Foundation for Autonomous Agents and MultiAgent Systems (IFAAMAS)

Place of publication

USA

Event title

AAMAS'19: 18th International Conference on Autonomous Agents and Multiagent Systems

Event Venue

Montreal, Canada

Date of Event (Start Date)

2019-05-13

Date of Event (End Date)

2019-05-17

Repository Status

  • Restricted

Socio-economic Objectives

Information systems, technologies and services not elsewhere classified

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