  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 crowd sourcing scenarios where
domain expertise is not required and only annotation guidelines are provided.
To alleviate these issues, we propose annotation curricula, a novel approach to
implicitly train annotators. Our goal is to gradually introduce annotators into
the task by ordering instances that are annotated according to a learning
curriculum. To do so, we first formalize annotation curricula for sentence- and
paragraph-level annotation tasks, define an ordering strategy, and identify
well-performing heuristics and interactively trained models on three existing
English datasets. We then conduct a user study with 40 voluntary participants
who are asked to identify the most fitting misconception for English tweets
about the Covid-19 pandemic. Our results show that using a simple heuristic to
order instances can already significantly reduce the total annotation time
while preserving a high annotation quality. Annotation curricula thus can
provide a novel way to improve data collection. To facilitate future research,
we further share our code and data consisting of 2,400 annotations.
