Modeling Random Guessing and Task Difficulty for Truth Inference in Crowdsourcing

被引：0

作者：

Yang, Yi ^{[1
]}

Bai, Quan ^{[2
]}

Liu, Qing ^{[3
]}

机构：

[1] Auckland Univ Technol, Auckland, New Zealand

[2] Univ Tasmania, Hobart, Tas, Australia

[3] CSIRO, Data61, Hobart, Tas, Australia

来源：

AAMAS '19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS | 2019年

关键词：

Crowdsourcing; Truth Inference; DISCOVERY;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper addresses the challenge of truth inference in crowd-sourcing 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.

引用

页码：2288 / 2290

页数：3

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