Design and Implementation of Relevance Assessments Using Crowdsourcing

Abstract

In the last years crowdsourcing has emerged as a viable platform for conducting relevance assessments. The main reason behind this trend is that makes possible to conduct experiments extremely fast, with good results and at low cost. However, like in any experiment, there are several details that would make an experiment work or fail. To gather useful results, user interface guidelines, inter-agreement metrics, and justification analysis are important aspects of a successful crowdsourcing experiment. In this work we explore the design and execution of relevance judgments using Amazon Mechanical Turk as crowdsourcing platform, introducing a methodology for crowdsourcing relevance assessments and the results of a series of experiments using TREC 8 with a fixed budget. Our findings indicate that workers are as good as TREC experts, even providing detailed feedback for certain query-document pairs. We also explore the importance of document design and presentation when performing relevance assessment tasks. Finally, we show our methodology at work with several examples that are interesting in their own.