Domain-independent quality measures for crowd truth disagreement

Abstract

Using crowdsourcing platforms such as CrowdFlower and Amazon Mechanical Turk for gathering human annotation data has become now a mainstream process. Such crowd involvement can reduce the time needed for solving an annotation task and with the large number of annotators can be a valuable source of annotation diversity. In order to harness this diversity across domains it is critical to establish a common ground for quality assessment of the results. In this paper we report our experiences for optimizing and adapting crowdsourcing microtasks across domains considering three aspects: (1) the micro-task template, (2) the quality measurements for the workers judgments and (3) the overall annotation workflow. We performed experiments in two domains, i.e. events extraction (MRP project) and medical relations extraction (Crowd-Watson project). The results confirm our main hypothesis that some aspects of the evaluation metrics can be defined in a domainindependent way for micro-tasks that assess the parameters to harness the diversity of annotations and the useful disagreement between workers. This paper focuses specifically on the parameters relevant for the 'event extraction' ground-truth data collection and demonstrates their reusability from the medical domain.