AAAI Publications, Fourth AAAI Conference on Human Computation and Crowdsourcing

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Why Is That Relevant? Collecting Annotator Rationales for Relevance Judgments
Tyler McDonnell, Matthew Lease, Mucahid Kutlu, Tamer Elsayed

Last modified: 2016-09-21


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.


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

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