Why Is That Relevant? Collecting Annotator Rationales for Relevance Judgments


  • Tyler McDonnell University of Texas at Austin
  • Matthew Lease University of Texas at Austin
  • Mucahid Kutlu Qatar University
  • Tamer Elsayed Qatar University




relevance judgments, task design, annotator agreement, rationale task, gold standard, standard task, stage task, pilot study, annotator rationale, experienced worker, data quality, mechanical turk, dual supervision


When collecting subjective human ratings of items, it can be difficult to measure and enforce data quality due to task subjectivity and lack of insight into how judges’ arrive at each rating decision. To address this, we propose requiring judges to provide a specific type of rationale underlying each rating decision. We evaluate this approach in the domain of Information Retrieval, where human judges rate the relevance of Webpages to search queries. Cost-benefit analysis over 10,000 judgments collected on Mechanical Turk suggests a win-win: experienced crowd workers provide rationales with almost no increase in task completion time while providing a multitude of further benefits, including more reliable judgments and greater transparency for evaluating both human raters and their judgments. Further benefits include reduced need for expert gold, the opportunity for dual-supervision from ratings and rationales, and added value from the rationales themselves.




How to Cite

McDonnell, T., Lease, M., Kutlu, M., & Elsayed, T. (2016). Why Is That Relevant? Collecting Annotator Rationales for Relevance Judgments. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 4(1), 139-148. https://doi.org/10.1609/hcomp.v4i1.13287