Annotation Curricula to Implicitly Train Non-Expert Annotators

Abstract

Annotation studies often require annotators to familiarize themselves with the task, its annotation scheme, and the data domain. This can be overwhelming in the beginning, mentally taxing, and induce errors into the resulting annotations; especially in citizen science or crowdsourcing scenarios where domain expertise is not required. To alleviate these issues, we propose annotation curricula, a novel approach to implicitly train annotators. The goal is to gradually introduce annotators into the task by ordering instances to be annotated according to a learning curriculum. To do so, we formalize annotation curricula for sentence- and paragraph-level annotation tasks, define an ordering strategy, and identifies well-performing heuristics and interactively trained models on three existing English datasets. Finally, we provide a proof of concept for annotation curricula in a carefully designed user study with 40 participants who are asked to identify the most fitting misconception for English tweets about the Covid-19 pandemic. The results indicate that using a simple heuristic to order instances can already significantly reduce the total annotation time while preserving a high annotation quality.