CrowdGP: a Gaussian Process Model for Inferring Relevance from Crowd Annotations

Test collection has been a crucial factor for developing information retrieval systems. Constructing a test collection requires annotators to assess the relevance of massive query-document pairs. Relevance annotations acquired through crowdsourcing platforms alleviate the enormous cost of this process but they are often noisy. Existing models to denoise crowd annotations mostly assume that annotations are generated independently, based on which a probabilistic graphical model is designed to model the annotation generation process. However, tasks are often correlated with each other in reality. It is an understudied problem whether and how task correlation helps in denoising crowd annotations. In this paper, we relax the independence assumption to model task correlation in terms of relevance. We propose a new crowd annotation generation model named CrowdGP, where true relevance labels, annotator competence, annotator’s bias towards relevancy, task difficulty, and task’s bias towards relevancy are modelled through a Gaussian process and multiple Gaussian variables respectively. The CrowdGP model shows better performance in terms of interring true relevance labels compared with state-of-the-art baselines on two crowdsourcing datasets on relevance. The experiments also demonstrate its effectiveness in terms of selecting new tasks for future crowd annotation, which is a new functionality of CrowdGP. Ablation studies indicate that the effectiveness is attributed to the modelling of task correlation based on the auxiliary information of tasks and the prior relevance information of documents to queries.