Multiannotator

Multiannotator is a labeling paradigm in which each data item is annotated by more than one independent annotator. This approach is widely used in machine learning, natural language processing, and computer vision to improve data quality, estimate ground truth, and quantify uncertainty in labels. It is commonly employed in crowdsourcing settings as well as in expert annotation tasks where redundancy helps detect errors and biases.

To derive a single ground-truth label from multiple annotations, researchers use aggregation methods. Simple majority voting assigns the label that appears most often. More sophisticated methods model annotator reliability and task difficulty. The Dawid-Skene model treats each annotator as having a confusion matrix and uses Expectation-Maximization to estimate the true label and each annotator’s error rates. Other probabilistic models, such as GLAD and various truth-discovery algorithms, jointly infer latent truth, annotator abilities, and task difficulty.

Evaluating multiannotator data often involves measuring inter-annotator agreement with statistics such as Cohen’s kappa (two raters), Fleiss’ kappa (multi-rater), or Krippendorff’s alpha. Beyond agreement, the ultimate goal is accurate ground truth, typically assessed on held-out data or via downstream performance.

Best practices include clear annotation guidelines, qualification tests for annotators, pilot rounds, and calibration tasks to align interpretations. It is also important to monitor quality, handle disagreements constructively, and consider the costs and potential biases of crowd workers. When used carefully, multiannotator labeling yields more reliable datasets and supports robust supervised learning, annotation of subjective domains, and reliable evaluation of models.