Gauging the Quality of Relevance Assessments using Inter-Rater Agreement

In recent years, gathering relevance judgments through non-topic originators has become an increasingly important problem in Information Retrieval. Relevance judgments can be used to measure the effectiveness of a system, and are often needed to build supervised learning models in learning-to-rank retrieval systems. The two most popular approaches to gathering bronze level judgments - where the judge is not the originator of the information need for which relevance is being assessed, and is not a topic expert - is through a controlled user study, or through crowdsourcing. However, judging comes at a cost (in time, and usually money) and the quality of the judgments can vary widely. In this work, we directly compare the reliability of judgments using three different types of bronze assessor groups. Our first group is a controlled Lab group; the second and third are two different crowdsourcing groups, CF-Document where assessors were free to judge any number of documents for a topic, and CF-Topic where judges were required to judge all of the documents from a single topic, in a manner similar to the Lab group. Our study shows that Lab assessors exhibit a higher level of agreement with a set of ground truth judgments than CF-Topic and CF-Document assessors. Inter-rater agreement rates show analogous trends. These finding suggests that in the absence of ground truth data, agreement between assessors can be used to reliably gauge the quality of relevance judgments gathered from secondary assessors, and that controlled user studies are more likely to produce reliable judgments despite being more costly.