Learning representations for quality estimation of crowdsourced submissions

doi:10.1016/j.ipm.2018.10.020

	Learning representations for quality estimation of crowdsourced submissions
	Lyu, Shanshan 1,2; Ouyang, Wentao 1; Shen, Huawei 1; Cheng, Xueqi 1
	2019-07-01
发表期刊	INFORMATION PROCESSING & MANAGEMENT
ISSN	0306-4573
卷号	56 期号:4 页码:1484-1493
摘要	The problem of quality estimation of crowdsourced work is of great importance. Although a variety of aggregation methods have been proposed to find high-quality structured claims in multiple-choice crowdsourcing tasks such as item labeling, they do not apply to more general tasks, such as article writing and brand design with unstructured submissions. One possibility to tackle this problem is to ask another set of crowd workers to review and grade each submission, essentially transforming unstructured submissions into structured ratings. Nevertheless, such an approach incurs unnecessary monetary cost and delay. In this paper, we address this problem by exploiting task requesters' historical feedback and directly modeling the submission quality. We propose two embedding-based methods where the first one learns worker embedding and the second one learns both worker embedding and meta information embedding, with additional consideration of neighborhood similarity. Experimental results on three large-scale crowdsourcing data sets demonstrate that our embedding-based feature-learning methods perform much better than feature-engineering methods that use popular learning-to-rank algorithms. At the same time, our methods do not require additional crowdsourced grading.
关键词	Crowdsourcing Quality estimation Embedding
DOI	10.1016/j.ipm.2018.10.020
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFB0803302] ; National Basic Research Program of China (973Program)[2014CB340401] ; National Natural Science Foundation of China[61602439] ; National Natural Science Foundation of China[61472400] ; National Natural Science Foundation of China[91746301] ; CAS Pioneer Hundred Talents Program[2920164120]
WOS研究方向	Computer Science ; Information Science & Library Science
WOS类目	Computer Science, Information Systems ; Information Science & Library Science
WOS记录号	WOS:000469907200020
出版者	ELSEVIER SCI LTD
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4208
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Lyu, Shanshan
作者单位	1.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Lyu, Shanshan,Ouyang, Wentao,Shen, Huawei,et al. Learning representations for quality estimation of crowdsourced submissions[J]. INFORMATION PROCESSING & MANAGEMENT,2019,56(4):1484-1493.
APA	Lyu, Shanshan,Ouyang, Wentao,Shen, Huawei,&Cheng, Xueqi.(2019).Learning representations for quality estimation of crowdsourced submissions.INFORMATION PROCESSING & MANAGEMENT,56(4),1484-1493.
MLA	Lyu, Shanshan,et al."Learning representations for quality estimation of crowdsourced submissions".INFORMATION PROCESSING & MANAGEMENT 56.4(2019):1484-1493.