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Relevance judgment of human assessors is inherently subjective and dynamicwhen evaluation datasets are created for Information Retrieval (IR) systems.However, a small group of experts' relevance judgment results are usually takenas ground truth to "objectively" evaluate the performance of the IR systems.Recent trends intend to employ a group of judges, such as outsourcing, toalleviate the potentially biased judgment results stemmed from using only asingle expert's judgment. Nevertheless, different judges may have differentopinions and may not agree with each other, and the inconsistency in humanrelevance judgment may affect the IR system evaluation results. In thisresearch, we introduce a Relevance Judgment Convergence Degree (RJCD) tomeasure the quality of queries in the evaluation datasets. Experimental resultsreveal a strong correlation coefficient between the proposed RJCD score and theperformance differences between the two IR systems.

Relevance Judgment Convergence Degree -- A Measure of Inconsistency among Assessors for Information Retrieval