Annotation Curricula to Implicitly Train Non-Expert Annotators

TL;DR

This paper introduces annotation curricula, a method to implicitly train non-expert annotators by ordering instances to reduce mental load and improve annotation efficiency, demonstrated through a user study on Covid-19 tweets.

Contribution

It formalizes annotation curricula for sentence and paragraph tasks, proposes an ordering strategy, and validates its effectiveness through experiments and a user study.

Findings

Ordering instances reduces annotation time significantly.

High annotation quality is maintained despite faster annotation.

Simple heuristics can effectively guide instance ordering.

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 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.