Measuring Annotator Agreement Generally across Complex Structured, Multi-object, and Free-text Annotation Tasks
This addresses the need for reliable quality assurance in data annotation for researchers and practitioners dealing with complex labeling tasks, representing an incremental improvement over existing measures.
The paper tackled the problem of measuring inter-annotator agreement for complex annotation tasks like structured, multi-object, and free-text annotations, proposing two novel measures that yield more consistent results across diverse tasks compared to existing methods.
When annotators label data, a key metric for quality assurance is inter-annotator agreement (IAA): the extent to which annotators agree on their labels. Though many IAA measures exist for simple categorical and ordinal labeling tasks, relatively little work has considered more complex labeling tasks, such as structured, multi-object, and free-text annotations. Krippendorff's alpha, best known for use with simpler labeling tasks, does have a distance-based formulation with broader applicability, but little work has studied its efficacy and consistency across complex annotation tasks. We investigate the design and evaluation of IAA measures for complex annotation tasks, with evaluation spanning seven diverse tasks: image bounding boxes, image keypoints, text sequence tagging, ranked lists, free text translations, numeric vectors, and syntax trees. We identify the difficulty of interpretability and the complexity of choosing a distance function as key obstacles in applying Krippendorff's alpha generally across these tasks. We propose two novel, more interpretable measures, showing they yield more consistent IAA measures across tasks and annotation distance functions.