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AuthorKutlu, Mucahid
AuthorMcDonnell, Tyler
AuthorSheshadri, Aashish
AuthorElsayed, Tamer
AuthorLease, Matthew
Available date2024-02-21T08:22:11Z
Publication Date2018-08
Publication NameCEUR Workshop Proceedings
CitationGoyal, T., McDonnell, T., Kutlu, M., Elsayed, T., & Lease, M. (2018, June). Your behavior signals your reliability: Modeling crowd behavioral traces to ensure quality relevance annotations. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (Vol. 6, pp. 41-49).
ISSN1613-0073
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85052640514&origin=inward
URIhttp://hdl.handle.net/10576/52017
AbstractCrowdsourcing offers an affordable and scalable means to collect relevance judgments for information retrieval test collections. However, crowd assessors may showhigher variance in judgment quality than trusted assessors. In this paper, we investigate how to effectively utilize both groups of assessors in partnership. We study how agreement in judging is correlated with three factors: relevance category, document rankings, and topical variance. Based on this, we then propose two collaborative judging methods in which some document-topic pairs are assigned to in-house assessors for relevance judging while the rest are assessed by crowd workers. Results on two TREC collections show encouraging results when we distribute work intelligently between our two groups of assessors.
SponsorThis work was made possible by NPRP grant# NPRP 7-1313-1-245 from the Qatar National Research Fund (a member of Qatar Foundation).
Languageen
PublisherCEUR-WS
SubjectCrowdsourcing
Evaluation
Information retrieval
Relevance
TitleMix and match: Collaborative expert-crowd judging for building test collections accurately and affordably
TypeConference Paper
Pagination41-49
Volume Number2167
dc.accessType Abstract Only


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